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  • 1.
    Aoga, John O. R.
    et al.
    Ecole Doctorale Science Pour Ingenieur, Université d’Abomey-Calavi, Abomey-Calavi, Benin.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Skövde Artificial Intelligence Lab (SAIL).
    Veljanoska, Stefanija
    Université de Rennes 1, CNRS/CREM-UMR621, Rennes, France.
    Nijssen, Siegfried
    ICTEAM, Université catholique de Louvain, Belgium.
    Schaus, Pierre
    ICTEAM, Université catholique de Louvain, Belgium.
    Impact of Weather Factors on Migration Intention Using Machine Learning Algorithms2024In: Operations Research Forum, E-ISSN 2662-2556, Vol. 5, no 1, article id 8Article in journal (Refereed)
    Abstract [en]

    A growing attention in the empirical literature has been paid on the incidence of climate shocks and change on migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks toward an individual’s intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We performed several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they influence the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) the weather features improve the prediction performance, although socioeconomic characteristics have more influence on migration intentions, (ii) a country-specific model is necessary, and (iii) the international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

  • 2.
    Athanasopoulos, George
    et al.
    Monash University, Australia.
    Hyndman, Rob J.
    Monash University, Australia.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Panagiotelis, Anastasios
    University of Sydney, Australia.
    Editorial: Innovations in hierarchical forecasting2024In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200Article in journal (Other academic)
  • 3.
    Athanasopoulos, George
    et al.
    Monash University, VIC 3145, Australia.
    Hyndman, Rob J.
    Monash University, VIC 3800, Australia.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Panagiotelis, Anastasios
    The University of Sydney, NSW 2006, Australia.
    Forecast reconciliation: A review2023In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200Article, review/survey (Refereed)
    Abstract [en]

    Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography. 

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  • 4.
    Athanasopoulos, George
    et al.
    Department of Econometrics and Business Statistics, Monash University, Australia.
    Hyndman, Rob. J.
    Department of Econometrics and Business Statistics, Monash University, Australia.
    Kourentzes, Nikolaos
    epartment of Management Science, Lancaster University Management School, United Kingdom.
    Petropoulos, Fotios
    Information, Decision and Operations Division, School of Management, University of Bath, United Kingdom.
    Forecasting with temporal hierarchies2017In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 262, no 1, p. 60-74Article in journal (Refereed)
    Abstract [en]

    This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from short-term operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments. 

  • 5.
    Athanasopoulos, George
    et al.
    Department of Econometrics and Business Statistics, Monash University, Australia.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    On the evaluation of hierarchical forecasts2023In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 39, no 4, p. 1502-1511Article in journal (Refereed)
    Abstract [en]

    The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of intermittency, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations. 

  • 6.
    Barrow, Devon K.
    et al.
    School of Strategy and Leadership, Faculty of Business and Law, Coventry University, Coventry, West Midlands, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Distributions of forecasting errors of forecast combinations: Implications for inventory management2016In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 177, p. 24-33Article in journal (Refereed)
    Abstract [en]

    Inventory control systems rely on accurate and robust forecasts of future demand to support decisions such as setting of safety stocks. The combination of multiple forecasts is shown to be effective not only in reducing forecast errors, but also in being less sensitive to limitations of a single model. Research on forecast combination has primarily focused on improving accuracy, largely ignoring the overall shape and distribution of forecast errors. Nonetheless, these are essential for managing the level of aversion to risk and uncertainty for companies. This study examines the forecast error distributions of base and combination forecasts and their implications for inventory performance. It explores whether forecast combinations transform the forecast error distribution towards desired properties for safety stock calculations, typically based on the assumption of normally distributed errors and unbiased forecasts. In addition, it considers the similarity between in- and out-of-sample characteristics of such errors and the impact of different lead times. The effects of established combination methods are explored empirically using a representative set of forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that forecast combinations make the in- and out-of-sample behaviour more consistent, requiring less safety stock on average than base forecasts. Furthermore we find that using in-sample empirical error distributions of combined forecasts approximates well the out-of-sample ones, in contrast to base forecasts. 

  • 7.
    Barrow, Devon
    et al.
    Faculty of Business, Environment and Society, Coventry University, Coventry, West Midlands, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    The impact of special days in call arrivals forecasting: A neural network approach to modelling special days2018In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 264, no 3, p. 967-977Article in journal (Refereed)
    Abstract [en]

    A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe's leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods. 

  • 8.
    Barrow, Devon
    et al.
    Birmingham Business School Department of Management, United Kingdom.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Lancaster University Management School Department of Management Science, United Kingdom.
    Sandberg, Rickard
    Stockholm School of Economics Center for Data Analytics, Sweden.
    Niklewski, Jacek
    Coventry University Faculty of Business, Environment and Society, United Kingdom.
    Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning2020In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 160, article id 113637Article in journal (Refereed)
    Abstract [en]

    A major challenge in automating the production of a large number of forecasts, as often required in many business applications, is the need for robust and reliable predictions. Increased noise, outliers and structural changes in the series, all too common in practice, can severely affect the quality of forecasting. We investigate ways to increase the reliability of exponential smoothing forecasts, the most widely used family of forecasting models in business forecasting. We consider two alternative sets of approaches, one stemming from statistics and one from machine learning. To this end, we adapt M-estimators, boosting and inverse boosting to parameter estimation for exponential smoothing.  We propose appropriate modifications that are necessary for time series forecasting while aiming to obtain scalable algorithms. We evaluate the various estimation methods using multiple real datasets and find that several approaches outperform the widely used maximum likelihood estimation. The novelty of this work lies in (1) demonstrating the usefulness of M-estimators, (2) and of inverse boosting, which outperforms standard boosting approaches, and (3) a comparative look at statistics versus machine learning inspired approaches.

  • 9.
    Brolin, Erik
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Högberg, Dan
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Scania CV, Södertälje, Sweden.
    Skewed Boundary Confidence Ellipses for Anthropometric Data2020In: DHM2020: Proceedings of the 6th International Digital Human Modeling Symposium, August 31 – September 2, 2020 / [ed] Lars Hanson, Dan Högberg, Erik Brolin, Amsterdam: IOS Press, 2020, p. 18-27Conference paper (Refereed)
    Abstract [en]

    Some anthropometric measurements, such as body weight often show a positively skewed distribution. Different types of transformations can be applied when handling skewed data in order to make the data more normally distributed. This paper presents and visualises how square root, log normal and, multiplicative inverse transformations can affect the data when creating boundary confidence ellipses. The paper also shows the difference of created manikin families, i.e. groups of manikin cases, when using transformed distributions or not, for three populations with different skewness. The results from the study show that transforming skewed distributions when generating confidence ellipses and boundary cases is appropriate to more accurately consider this type of diversity and correctly describe the shape of the actual skewed distribution. Transforming the data to create accurate boundary confidence regions is thought to be advantageous, as this would create digital manikins with enhanced accuracy that would produce more realistic and accurate simulations and evaluations when using DHM tools for the design of products and workplaces.

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  • 10.
    Fildes, Robert
    et al.
    Lancaster Centre for Forecasting, Lancaster University, Department of Management Science, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster Centre for Forecasting, Lancaster University, Department of Management Science, United Kingdom.
    Validation and forecasting accuracy in models of climate change2011In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 27, no 4, p. 968-995Article in journal (Refereed)
    Abstract [en]

    Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster's perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.

  • 11.
    Gellerstedt, Martin
    Institute of Biomedicine, The Sahlgrenska Academy at Göteborg University, Sweden.
    Interpretation of diagnostic information given patient characteristics2006Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

     The aim with this thesis was to describe, exemplify and develop theory for reference values and diagnostic tests, especially focusing on the variability between individuals. To facilitate interpretation of medical information it is common to establish some kind of limit. There are several different rationales for the choice of such a limit. Reference values are intended to be solely descriptive, while medical decision limits are used for identification of a present or future disorder. The frequently used bimodal model can be used not only for discrimination between healthy vs diseased but for separation of other conditions as well. Reference values for amplitude of accommodation among school children were suggested based on a bimodal model discriminating between children with vs without symptoms occurring at near work. If the variability between individuals is high compared to the variability found within an individual or if the diagnostic information is subjective, it may be favorable to use the individual as its own reference. The diagnosis of food-hypersensitivity for patients with subjective symptoms was used as an illustration. A pre-defined approach for interpretation of case records gave high inter-observer reliability, and gave different diagnoses than a previously used approach. To harmonize the sensitivity and specificity of reference values across subpopulations, partitioning of reference values is one possibility. Existing criteria are limited to the consideration of only two subpopulations. A computer assisted procedure for considering partitioning of several subpopulations was developed. The potential relationship between diagnostic accuracy of a test and other factors are highlighted in diagnostic theory. However, there is no advice regarding how to adjust for this relationship. Two possibilities have been presented; to use a multivariate model including interactions or to use different thresholds for different subpopulations. Diagnostic information could be individually adjusted by using a prevalence function which estimate probability of target disorder, given patient characteristics. A computer based decision support system including such a prevalence function was shown to have potential benefits for assisting medical decisions.

  • 12.
    Gellerstedt, Martin
    Högskolan Väst, Sverige.
    M12: medicinsk statistik2004 (ed. 1)Book (Other academic)
  • 13.
    Gellerstedt, Martin
    Department for Studies of Work, Economics and Health, University of Trollhättan, Uddevalla, Sweden.
    Statistical issues: significantly important in medical research2002In: Allergy. European Journal of Allergy and Clinical Immunology, ISSN 0105-4538, E-ISSN 1398-9995, Vol. 57, no 2, p. 76-82Article, review/survey (Other academic)
  • 14.
    Gellerstedt, Martin
    Högskolan Väst, Sverige.
    Statistiska metoder för kvalitetsutveckling1997 (ed. 1)Book (Other academic)
  • 15.
    Gellerstedt, Martin
    et al.
    Dept. of Economics and IT, University West Sweden, Sweden.
    Svensson, Lars
    Dept. of Economics and IT, University West Sweden, Sweden.
    Www means win win win in education: some experiences from online courses in applied statistics2010In: OZCOTS 2010: Proceedings of the 7th Australian Conferenceon Teaching Statistics / [ed] Helen MacGillivray; Brian Phillips, Statistical Society of Australia , 2010, p. 51-56Conference paper (Refereed)
    Abstract [en]

    This paper reports on the experiences from online courses in applied statistics. The courses were designed with the ambition of making studies in statistics, fun, interesting, useful, not that difficult and directly supported the possibility to combine studies and work. When designing the courses we considered three dimensions: "pedagogies","community" and "structure". Experiences after giving a first-year course three times show that the online course succeeds in attracting new studen ts since 90% of the participants would not be able to follow an on-campus course and 62% worked full time.

    The pedagogies were highly appreciated because focusing on the interpretation of results and using computer analyses really changed the prejudices about statistics. Structure and prompt feedback was experienced as important factors. It was possible to combine online studies with employment, and the student completion rate was (84%, 55% and 61%), with a potential for further improvements.

  • 16.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Pashami, Sepideh
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Incremental causal discovery and visualization2019In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019, p. 1-6Conference paper (Refereed)
    Abstract [en]

    Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. 

  • 17.
    Kourentzes, Nikolaos
    Department of Management Science, Lancaster University Management School, Lancaster, Lancashire, United Kingdom.
    Intermittent demand forecasts with neural networks2013In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 143, no 1, p. 198-206Article in journal (Refereed)
    Abstract [en]

    Intermittent demand appears when demand events occur only sporadically. Typically such time series have few observations making intermittent demand forecasting challenging. Forecast errors can be costly in terms of unmet demand or obsolescent stock. Intermittent demand forecasting has been addressed using established forecasting methods, including simple moving averages, exponential smoothing and Croston's method with its variants. This study proposes a neural network (NN) methodology to forecast intermittent time series. These NNs are used to provide dynamic demand rate forecasts, which do not assume constant demand rate in the future and can capture interactions between the non-zero demand and the inter-arrival rate of demand events. This overcomes the limitations of Croston's method. In order to mitigate the issue of limited fitting sample, which is common in intermittent demand, the proposed models use regularised training and median ensembles over multiple training initialisations to produce robust forecasts. The NNs are evaluated against established benchmarks using both forecasting accuracy and inventory metrics. The findings of forecasting and inventory metrics are conflicting. While NNs achieved poor forecasting accuracy and bias, all NN variants achieved higher service levels than the best performing Croston's method variant, without requiring analogous increases in stock holding volume. Therefore, NNs are found to be effective for intermittent demand applications. This study provides further arguments and evidence against the use of conventional forecasting accuracy metrics to evaluate forecasting methods for intermittent demand, concluding that attention to inventory metrics is desirable. 

  • 18.
    Kourentzes, Nikolaos
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Department of Management Science, Lancaster University Management School, Bailrigg, Lancaster, United Kingdom.
    Athanasopoulos, George
    Department of Econometrics and Business Statistics, Monash University, Caulfield East, Australia.
    Elucidate structure in intermittent demand series2021In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 288, no 1, p. 141-152Article in journal (Refereed)
    Abstract [en]

    Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.

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  • 19.
    Kourentzes, Nikolaos
    et al.
    Department of Management Science, Lancaster University Management School, Lancaster University, United Kingdom.
    Barrow, Devon
    Faculty of Business, Environment and Society, Coventry University, United Kingdom.
    Petropoulos, Fotios
    School of Management, University of Bath, United Kingdom.
    Another look at forecast selection and combination: Evidence from forecast pooling2019In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 209, p. 226-235Article in journal (Refereed)
    Abstract [en]

    Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as mitigating modelling uncertainty as we are not forced to choose a single model. However, whether all forecasts to be combined are appropriate, or not, is typically overlooked and various weighting schemes have been proposed to lessen the impact of inappropriate forecasts. We argue that selecting a reasonable pool of forecasts is fundamental in the modelling process and in this context both forecast selection and combination can be seen as two extreme pools of forecasts. We evaluate forecast pooling approaches and find them beneficial in terms of forecast accuracy. We propose a heuristic to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination. 

  • 20.
    Kourentzes, Nikolaos
    et al.
    Lancaster University Management School, Department of Management Science, Lancaster, Lancashire, United Kingdom.
    Petropoulos, Fotios
    Logistics and Operations Management Section, Cardiff Business School, Cardiff University, United Kingdom.
    Forecasting with multivariate temporal aggregation: The case of promotional modelling2016In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 181, p. 145-153Article in journal (Refereed)
    Abstract [en]

    Demand forecasting is central to decision making and operations in organisations. As the volume of forecasts increases, for example due to an increased product customisation that leads to more SKUs being traded, or a reduction in the length of the forecasting cycle, there is a pressing need for reliable automated forecasting. Conventionally, companies rely on a statistical baseline forecast that captures only past demand patterns, which is subsequently adjusted by human experts to incorporate additional information such as promotions. Although there is evidence that such process adds value to forecasting, it is questionable how much it can scale up, due to the human element. Instead, in the literature it has been proposed to enhance the baseline forecasts with external well-structured information, such as the promotional plan of the company, and let experts focus on the less structured information, thus reducing their workload and allowing them to focus where they can add most value. This change in forecasting support systems requires reliable multivariate forecasting models that can be automated, accurate and robust. This paper proposes an extension of the recently proposed Multiple Aggregation Prediction Algorithm (MAPA), which uses temporal aggregation to improve upon the established exponential smoothing family of methods. MAPA is attractive as it has been found to increase both the accuracy and robustness of exponential smoothing. The extended multivariate MAPA is evaluated against established benchmarks in modelling a number of heavily promoted products and is found to perform well in terms of forecast bias and accuracy. Furthermore, we demonstrate that modelling time series using multiple temporal aggregation levels makes the final forecast robust to model mis-specification. 

  • 21.
    Kourentzes, Nikolaos
    et al.
    Lancaster University Management School, Department of Management Science, Lancaster, Lancashire, United Kingdom.
    Petropoulos, Fotios
    Lancaster University Management School, Department of Management Science, Lancaster, Lancashire, United Kingdom.
    Trapero, Juan R.
    Universidad de Castilla-La Mancha, Departamento de Administracion de Empresas, Ciudad Real, Spain.
    Improving forecasting by estimating time series structural components across multiple frequencies2014In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 30, no 2, p. 291-302Article in journal (Refereed)
    Abstract [en]

    Identifying the most appropriate time series model to achieve a good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy. Multiple time series are constructed from the original time series, using temporal aggregation. These derivative series highlight different aspects of the original data, as temporal aggregation helps in strengthening or attenuating the signals of different time series components. In each series, the appropriate exponential smoothing method is fitted and its respective time series components are forecast. Subsequently, the time series components from each aggregation level are combined, then used to construct the final forecast. This approach achieves a better estimation of the different time series components, through temporal aggregation, and reduces the importance of model selection through forecast combination. An empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts. 

  • 22.
    Kourentzes, Nikolaos
    et al.
    Lancaster University Management School Department of Management Science, Lancaster, United Kingdom.
    Rostami-Tabar, Bahman
    Cardiff Business School, Cardiff University, Cardiff, United Kingdom.
    Barrow, Devon K.
    School of Strategy and Leadership, Faculty of Business and Law Coventry University, Coventry, West Midlands, United Kingdom.
    Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?2017In: Journal of Business Research, ISSN 0148-2963, E-ISSN 1873-7978, Vol. 78, p. 1-9Article in journal (Refereed)
    Abstract [en]

    Recent advances have demonstrated the benefits of temporal aggregation for demand forecasting, including increased accuracy, improved stock control and reduced modelling uncertainty. With temporal aggregation a series is transformed, strengthening or attenuating different elements and thereby enabling better identification of the time series structure. Two different schools of thought have emerged. The first focuses on identifying a single optimal temporal aggregation level at which a forecasting model maximises its accuracy. In contrast, the second approach fits multiple models at multiple levels, each capable of capturing different features of the data. Both approaches have their merits, but so far they have been investigated in isolation. We compare and contrast them from a theoretical and an empirical perspective, discussing the merits of each, comparing the realised accuracy gains under different experimental setups, as well as the implications for business practice. We provide suggestions when to use each for maximising demand forecasting gains. 

  • 23.
    Kourentzes, Nikolaos
    et al.
    Lancaster University Management School, United Kingdom.
    Sagaert, Yves
    Faculty of Engineering and Architecture of Ghent University, Belgium.
    Incorporating Leading Indicators into Sales Forecasts2018In: Foresight: The International Journal of Applied Forecasting, ISSN 1555-9068, no 48, p. 24-30Article in journal (Refereed)
    Abstract [en]

    Using leading indicators for business forecasting-in contrast to macroeconomic forecasting-has been relatively rare, partly because our traditional time-series methods do not readily allow incorporation of external variables. Nowadays, however, we have an abundance of potentially useful indicators, and there is evidence that utilizing relevant ones in a forecasting model can significantly improve forecast accuracy and transparency. In this article, Nikolaos and Yves show how to find appropriate leading indicators and make good use of them for sales forecasting. 

  • 24.
    Luz Gámiz, María
    et al.
    Department of Statistics and O.R., University of Granada, Spain.
    Kalén, Anton
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Universidade de Vigo, Pontevedra, Spain.
    Nozal-Cañadas, Rafael
    Department of Computer Science, UiT-The Artic University of Norway, Tromso, Norway.
    Raya-Miranda, Rocío
    Department of Statistics and O.R., University of Granada, Spain.
    Statistical supervised learning with engineering data: A case study of low frequency noise measured on semiconductor devices2022In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 120, no 9-10, p. 5835-5853Article in journal (Refereed)
    Abstract [en]

    Our practical motivation is the analysis of potential correlations between spectral noise current and threshold voltage from common on-wafer MOSFETs. The usual strategy leads to the use of standard techniques based on Normal linear regression easily accessible in all statistical software (both free or commercial). However, these statistical methods are not appropriate because the assumptions they lie on are not met. More sophisticated methods are required. A new strategy based on the most novel nonparametric techniques which are data-driven and thus free from questionable parametric assumptions is proposed. A backfitting algorithm accounting for random effects and nonparametric regression is designed and implemented. The nature of the correlation between threshold voltage and noise is examined by conducting a statistical test, which is based on a novel technique that summarizes in a color map all the relevant information of the data. The way the results are presented in the plot makes it easy for a non-expert in data analysis to understand what is underlying. The good performance of the method is proven through simulations and it is applied to a data case in a field where these modern statistical techniques are novel and result very efficient.

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  • 25.
    Mellin, Jonas
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Andler, Sten F.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    The effect of optimizing engine control on fuel consumption and roll amplitude in ocean-going vessels: An experimental study2015Report (Other academic)
    Abstract [en]

    We use data-generated models based on data from experiments of an ocean-going vessel to study the effect of optimizing fuel consumption. The optimization is an add-on module to the existing diesel-engine fuel-injection control built by Q-TAGG R&D AB. The work is mainly a validation of knowledge-based models based on a priori knowledge from physics. The results from a simulation-based analysis of the predictive models built on data agree with the results based on knowledge-based models in a companion study. This indicates that the optimization algorithm saves fuel. We also address specific problems of adapting data to existing machine learning methods. It turns out that we can simplify the problem by ignoring the auto-correlative effects in the time series by employing low-pass filters and resampling techniques. Thereby we can use mature and robust classification techniques with less requirements on the data to demonstrate that fuel is saved compared to the full-fledged time series analysis techniques which are harder to use. The trade-off is the accuracy of the result, that is, it is hard to tell exactly how much fuel is saved. In essence, however, this process can be automated due to its simplicity. 

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  • 26.
    Ng, Amos H. C.
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Siegmund, Florian
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Deb, Kalyanmoy
    Michigan State University, East Lansing, Michigan, USA.
    Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement2018In: Journal of Systems and Information Technology, ISSN 1328-7265, E-ISSN 1758-8847, Vol. 20, no 4, p. 489-512Article in journal (Refereed)
    Abstract [en]

    Purpose

    Stochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating production systems improvement problems into multi-objective optimizations has provided the possibility to predict the optimal trade-offs between improvement costs and system performance, before making the final decision for implementation. However, the fact that stochastic simulations rely on running a large number of replications to cope with the randomness and obtain some accurate statistical estimates of the system outputs, has posed a serious issue for using this kind of multi-objective optimization in practice, especially with complex models. Therefore, the purpose of this study is to investigate the performance enhancements of a reference point based evolutionary multi-objective optimization algorithm in practical production systems improvement problems, when combined with various dynamic re-sampling mechanisms.

    Design/methodology/approach

    Many algorithms consider the preferences of decision makers to converge to optimal trade-off solutions faster. There also exist advanced dynamic resampling procedures to avoid wasting a multitude of simulation replications to non-optimal solutions. However, very few attempts have been made to study the advantages of combining these two approaches to further enhance the performance of computationally expensive optimizations for complex production systems. Therefore, this paper proposes some combinations of preference-based guided search with dynamic resampling mechanisms into an evolutionary multi-objective optimization algorithm to lower both the computational cost in re-sampling and the total number of simulation evaluations.

    Findings

    This paper shows the performance enhancements of the reference-point based algorithm, R-NSGA-II, when augmented with three different dynamic resampling mechanisms with increasing degrees of statistical sophistication, namely, time-based, distance-rank and optimal computing buffer allocation, when applied to two real-world production system improvement studies. The results have shown that the more stochasticity that the simulation models exert, the more the statistically advanced dynamic resampling mechanisms could significantly enhance the performance of the optimization process.

    Originality/value

    Contributions of this paper include combining decision makers’ preferences and dynamic resampling procedures; performance evaluations on two real-world production system improvement studies and illustrating statistically advanced dynamic resampling mechanism is needed for noisy models.

  • 27.
    Petropoulos, Fotios
    et al.
    Cardiff Business School of Cardiff University, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University, United Kingdom.
    Commentary: Two Sides of the Same Coin2016In: Foresight: The International Journal of Applied Forecasting, ISSN 1555-9068, no 42, p. 37-39Article in journal (Refereed)
  • 28.
    Petropoulos, Fotios
    et al.
    Lancaster Centre for Forecasting, Lancaster University Management School, Lancaster University, Lancaster, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster Centre for Forecasting, Lancaster University Management School, Lancaster University, Lancaster, United Kingdom.
    Forecast combinations for intermittent demand2015In: Journal of the Operational Research Society, ISSN 0160-5682, E-ISSN 1476-9360, Vol. 66, no 6, p. 914-924Article in journal (Refereed)
    Abstract [en]

    Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, the effects of combining, which is a standard practice for regular demand, have not been investigated. This paper empirically explores the efficiency of forecast combinations in the intermittent demand context. We examine both method and temporal combinations of forecasts. The first are based on combinations of different methods on the same time series, while the latter use combinations of forecasts produced on different views of the time series, based on temporal aggregation. Temporal combinations of single or multiple methods are investigated, leading to a new time-series classification, which leads to model selection and combination. Results suggest that appropriate combinations lead to improved forecasting performance over single methods, as well as simplifying the forecasting process by limiting the need for manual selection of methods or hyper-parameters of good performing benchmarks. This has direct implications for intermittent demand forecasting in practice. 

  • 29.
    Petropoulos, Fotios
    et al.
    Lancaster University, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University, United Kingdom.
    Improving Forecasting via Multiple Temporal Aggregation2014In: Foresight: The International Journal of Applied Forecasting, ISSN 1555-9068, no 34, p. 12-17Article in journal (Refereed)
    Abstract [en]

    In most business forecasting applications, the decision-making need we have directs the frequency of the data we collect (monthly, weekly, etc.) and use for forecasting. In this article, Fotios and Nikolaos introduce an approach that combines forecasts generated by modeling the different frequencies (levels of temporal aggregation). Their technique augments our information about the data used for forecasting and, as such, can result in more accurate forecasts. It also automatically reconciles the forecasts at different levels.

  • 30.
    Petropoulos, Fotios
    et al.
    Logistics and Operations Management Section, Cardiff Business School, Cardiff University, United Kingdom.
    Kourentzes, Nikolaos
    Department of Management Science, Lancaster University Management School, Lancaster University, United Kingdom.
    Nikolopoulos, Konstantinos
    Bangor Business School, Bangor University, United Kingdom.
    Another look at estimators for intermittent demand2016In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 181, p. 154-161Article in journal (Refereed)
    Abstract [en]

    In this paper we focus on forecasting for intermittent demand data. We propose a new aggregation framework for intermittent demand forecasting that performs aggregation over the demand volumes, in contrast to the standard framework that employs temporal (over time) aggregation. To achieve this we construct a transformed time series, the inverse intermittent demand series. The new algorithm is expected to work best on erratic and lumpy demand, as a result of the variance reduction of the non-zero demands. The improvement in forecasting performance is empirically demonstrated through an extensive evaluation in more than 8000 time series of two well-researched spare parts data sets from the automotive and defence sectors. Furthermore, a simulation is performed so as to provide a stock-control evaluation. The proposed framework could find popularity among practitioners given its suitability when dealing with clump sizes. As such it could be used in conjunction with existing popular forecasting methods for intermittent demand as an exception handling mechanism when certain types of demand are observed.

  • 31.
    Petropoulos, Fotios
    et al.
    School of Management, University of Bath, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University Management School, Lancaster University, United Kingdom.
    Nikolopoulos, Konstantinos
    Bangor Business School, Bangor University, United Kingdom.
    Siemsen, Enno
    Wisconsin School of Business, University of Wisconsin, USA.
    Judgmental selection of forecasting models2018In: Journal of Operations Management, ISSN 0272-6963, E-ISSN 1873-1317, Vol. 60, p. 34-46Article in journal (Refereed)
    Abstract [en]

    In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.

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  • 32.
    Petropoulos, Fotios
    et al.
    School of Management, University of Bath, United Kingdom.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Nikolopoulos, Konstantinos
    Durham University Business School, United Kingdom.
    Siemsen, Enno
    Wisconsin School of Business, University of Wisconsin, Madison, WI, USA.
    Judgmental selection of forecasting models (reprint)2023In: Judgment in Predictive Analytics / [ed] Matthias Seifert, Cham: Springer, 2023, Vol. 343, p. 53-84Chapter in book (Refereed)
    Abstract [en]

    In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.

  • 33.
    Pritularga, Kandrika F.
    et al.
    Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, United Kingdom.
    Svetunkov, Ivan
    Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, United Kingdom.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Shrinkage estimator for exponential smoothing models2023In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 39, no 3, p. 1351-1365Article in journal (Refereed)
    Abstract [en]

    Exponential smoothing is widely used in practice and has shown its efficacy and reliability in many business applications. Yet there are cases, for example when the estimation sample is limited, where the estimated smoothing parameters can be erroneous, often unnecessarily large. This can lead to over-reactive forecasts and high forecast errors. Motivated by these challenges, we investigate the use of shrinkage estimators for exponential smoothing. This can help with parameter estimation and mitigating parameter uncertainty. Building on the shrinkage literature, we explore ℓ1 and ℓ2 shrinkage for different time series and exponential smoothing model specifications. From a simulation and an empirical study, we find that using shrinkage in exponential smoothing results in forecast accuracy improvements and better prediction intervals. In addition, using bias–variance decomposition, we show the interdependence between smoothing parameters and initial values, and the importance of the initial value estimation on point forecasts and prediction intervals. 

  • 34.
    Pritularga, Kandrika F.
    et al.
    Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, UK, United Kingdom.
    Svetunkov, Ivan
    Centre for Marketing Analytics and Forecasting, Department of Management Science, Lancaster University Management School, UK, United Kingdom.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Stochastic coherency in forecast reconciliation2021In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 240, article id 108221Article in journal (Refereed)
    Abstract [en]

    Hierarchical forecasting has been receiving increasing attention in the literature. The notion of coherency is central to this, which implies that the hierarchical time series follows some linear aggregation constraints. This notion, however, does not take several modelling uncertainties into account. We propose to redefine coherency as stochastic. This allows to accommodate overlooked uncertainties in forecast reconciliation. We show analytically that there are two potential sources of uncertainty in forecast reconciliation. We use simulated data to demonstrate how these uncertainties propagate to the covariance matrix estimation, introducing uncertainty in the reconciliation weights matrix. This then increases the uncertainty of the reconciled forecasts. We apply our understanding to modelling accident and emergency admissions in a UK hospital. Our analysis confirms the insights from stochastic coherency in forecast reconciliation. It shows that we gain accuracy improvement from forecast reconciliation, on average, at the cost of the variability of the forecast error distribution. Users may opt to prefer less volatile error distributions to assist decision making. 

  • 35.
    Sagaert, Yves R.
    et al.
    Univ Ghent, Dept Ind Syst Engn & Prod Design, Ghent, Belgium / Solventure NV, Ghent, Belgium.
    Aghezzaf, El-Houssaine
    Univ Ghent, Dept Ind Syst Engn & Prod Design, Ghent, Belgium / Flanders Make, Lommel, Belgium.
    Kourentzes, Nikolaos
    Univ Lancaster, Sch Management, Dept Management Sci, Lancaster, United Kingdom.
    Desmet, Bram
    Solventure NV, Ghent, Belgium.
    Temporal big data for tactical sales forecasting in the tire industry2018In: Interfaces, ISSN 0092-2102, E-ISSN 1526-551X, Vol. 48, no 2, p. 121-129Article in journal (Refereed)
    Abstract [en]

    We propose a forecasting method to improve the accuracy of tactical sales predictions for a major supplier to the tire industry. This level of forecasting, which serves as direct input to the demand-planning process and steers the global supply chain, is typically done up to a year in advance. The product portfolio of the company for which we did our research is sensitive to external events. Univariate statistical methods, which are commonly used in practice, cannot be used to anticipate and forecast changes in the market; and forecasts by human experts are known to be biased and inconsistent. The method we propose allows us to automate the identification of key leading indicators, which drive sales, from a massive set of macroeconomic indicators, across di erent regions and markets; thus, we can generate accurate forecasts. Our method also allows us to handle the additional complexity that results from short-term and long-term dynamics of product sales and external indicators. For the company we study, accuracy improved by 16.1 percent over its current practice. Furthermore, our method makes the market dynamics transparent to company managers, thus allowing them to better understand the events and economic variables that a ect the sales of their products. 

  • 36.
    Saoud, Patrick
    et al.
    Lancaster University Management School, Department of Management Science, United Kingdom.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Boylan, John E.
    Lancaster University Management School, Department of Management Science, United Kingdom.
    Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation2022In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 110, article id 102614Article in journal (Refereed)
    Abstract [en]

    Safety stock is necessary for firms in order to manage the uncertainty of demand. A key component in its determination is the estimation of the variance of the forecast error over lead time. Given the multitude of demand processes that lack analytical expressions of the variance of forecast error, an approximation is needed. It is common to resort to finding the one-step ahead forecast errors variance and scaling it by the lead time. However, this approximation is flawed for many processes as it overlooks the autocorrelations that arise between forecasts made at different lead times. This research addresses the issue of these correlations first by demonstrating their existence for some fundamental demand processes, and second by showing through an inventory simulation the inadequacy of the approximation. We propose to monitor the empirical variance of the lead time errors, instead of estimating the point forecast error variance and extending it over the lead time interval. The simulation findings indicate that this approach provides superior results to other approximations in terms of cycle-service level. Given its lack of assumptions and computational simplicity, it can be easily implemented in any software, making it appealing to both practitioners and academics.

  • 37.
    Spiliotis, Evangelos
    et al.
    Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
    Petropoulos, Fotios
    School of Management, University of Bath, Bath, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Assimakopoulos, Vassilios
    Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
    Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption2020In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 261, article id 114339Article in journal (Refereed)
    Abstract [en]

    Achieving high accuracy in energy consumption forecasting is critical for improving energy management and planning. However, this requires the selection of appropriate forecasting models, able to capture the individual characteristics of the series to be predicted, which is a task that involves a lot of system and region level, not only the model selection problem is expanded to multiple time series, but we also require aggregation consistency of the forecasts across levels. Although hierarchical forecasting, such as the bottom-up, the top-down, and the optimal reconciliation methods, can address the aggregation consistency concerns, it does not resolve the model selection uncertainty. To address this issue, we rely on Multiple Temporal Aggregation (MTA), which has been shown to mitigate the model selection problem for low-frequency time series. We propose a modification of the Multiple Aggregation Prediction Algorithm, a special implementation of MTA, for high-frequency time series to better handle the undesirable effect of seasonality shrinkage that MTA implies and combine it with conventional cross-sectional hierarchical forecasting. The impact of incorporating temporal aggregation in hierarchical forecasting is empirically assessed using a real data set from five bank branches. We show that the proposed MTA approach, combined with the optimal reconciliation method, demonstrates superior accuracy, aggregation consistency, and reliable automatic forecasting. 

  • 38.
    Sroginis, Anna
    et al.
    Lancaster University Management School, Department of Management Science, Lancaster, LA1 4YX, United Kingdom.
    Fildes, Robert
    Lancaster University Management School, Department of Management Science, Lancaster, LA1 4YX, United Kingdom.
    Kourentzes, Nikolaos
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Use of contextual and model-based information in adjusting promotional forecasts2023In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 307, no 3, p. 1177-1191Article in journal (Refereed)
    Abstract [en]

    Despite improvements in statistical forecasting, human judgment remains fundamental to business forecasting and demand planning. Typically, forecasters do not rely solely on statistical forecasts; they also adjust forecasts according to their knowledge, experience, and information that is not available to statistical models. However, we have limited understanding of the adjustment mechanisms employed, particularly how people use additional information (e.g., special events and promotions, weather, holidays) and under which conditions this is beneficial. Using a multi-method approach, we first analyse a UK retailer case study exploring its operations and the forecasting process. The case study provides a contextual setting for the laboratory experiments that simulate a typical supply chain forecasting process. In the experimental study, we provide past sales, statistical forecasts (using baseline and promotional models) and qualitative information about past and future promotional periods. We include contextual information, with and without predictive value, that allows us to investigate whether forecasters can filter such information correctly. We find that when adjusting, forecasters tend to focus on model-based anchors, such as the last promotional uplift and the current statistical forecast, ignoring past baseline promotional values and additional information about previous promotions. The impact of contextual statements for the forecasting period depends on the type of statistical predictions provided: when a promotional forecasting model is presented, people tend to misinterpret the provided information and over-adjust, harming accuracy. 

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  • 39.
    Svensson, C.
    et al.
    Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Hegrestad, A.-L.
    Växa Sverige, Falköping, Sweden.
    Lindblom, Jessica
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Department of Information Technology, Uppsala University, Sweden.
    Dairy farmer and farm staff attitudes and perceptions regarding daily milk allowance to calves2023In: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198, Vol. 106, no 10, p. 7220-7239Article in journal (Refereed)
    Abstract [en]

    The benefits of feeding calves more milk are increasingly being recognized by dairy farmers. However, most producers have still not implemented higher feeding plans. The aim of the present study was to gain a deeper understanding of farmer and farm staff attitudes, and the perceptions and factors considered in their decision-making regarding daily milk allowances. We collected data through focus group interviews with dairy farmers, farm managers, and calf-care workers who were selected using purposive and snowball sampling. In total, 40 persons (24 women and 16 men) joined a focus group interview (6 in all, each with 5–8 participants). Interviews were recorded, and recordings were transcribed and analyzed thematically. Participants had contrasting opinions about the minimum, maximum, and recommended daily milk allowances to their calves. Their suggested lowest daily milk allowance to sustain animal welfare ranged from 4 to 8–10 L and the maximum allowance from 6 to 15 L. We found that farmers' and farm staff's choices and recommendations of milk-feeding protocols were influenced by a large number of factors that could be grouped into 4 themes: (1) Life beyond work, (2) Farm facilities and equipment, (3) Care of the calves, and (4) Profitability and production. Participants' considerations were similar and aimed to maximize daily milk allowance based on farm conditions. However, the allowances they described as optimal for their calves often differed from what they considered practically feasible. We found that the care of the calves and the well-being of the owners and the staff was central in the participants' decision-making, but that this care perspective was challenged by the social and economic sustainability of the farm. Most participants fed their calves twice daily and did not think that increasing that number would be practically feasible. Our results indicate that the participants' viewpoints regarding calves were important for their decision-making about milk allowances. We suggest that a more holistic perspective should be used when advising farmers about milk allowances, putting particular emphasis on the caring and social sustainability aspects of the individual farm. 

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  • 40.
    Sweidan, Dirar
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Johansson, Ulf
    Jönköping University, Department of Computing, Sweden..
    Alenljung, Beatrice
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Gidenstam, Anders
    University of Borås, Department of Information Technololgy, Sweden..
    Improved Decision Support for Product Returns using Probabilistic Prediction2023Conference paper (Refereed)
    Abstract [en]

    Product returns are not only costly for e-tailers, but the unnecessary transports also impact the environment. Consequently, online retailers have started to formulate policies to reduce the number of returns. Determining when and how to act is, however, a delicate matter, since a too harsh approach may lead to not only the order being cancelled, but also the customer leaving the business. Being able to accurately predict which orders that will lead to a return would be a strong tool, guiding which actions to be taken. This paper addresses the problem of data-driven product return prediction, by conducting a case study using a large real-world data set. The main results are that well-calibrated probabilistic predictors are essential for providing predictions with high precision and reasonable recall. This implies that utilizing calibrated models to predict some instances, while rejecting to predict others can be recommended. In practice, this would make it possible for a decision-maker to only act upon a subset of all predicted returns, where the risk of a return is very high.

  • 41.
    Trapero, Juan R.
    et al.
    Universidad de Castilla-La Mancha, Department of Business Administration, Ciudad Real, Spain.
    Cardós, Manuel
    Universidad Politécnica de Valencia, Department of Business Administration, Valencia, Spain.
    Kourentzes, Nikolaos
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Empirical safety stock estimation based on kernel and GARCH models2019In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 84, p. 199-211Article in journal (Refereed)
    Abstract [en]

    Supply chain risk management has drawn the attention of practitioners and academics alike. One source of risk is demand uncertainty. Demand forecasting and safety stock levels are employed to address this risk. Most previous work has focused on point demand forecasting, given that the forecast errors satisfy the typical normal i.i.d. assumption. However, the real demand for products is difficult to forecast accurately, which means that—at minimum—the i.i.d. assumption should be questioned. This work analyzes the effects of possible deviations from the i.i.d. assumption and proposes empirical methods based on kernel density estimation (non-parametric) and GARCH(1,1) models (parametric), among others, for computing the safety stock levels. The results suggest that for shorter lead times, the normality deviation is more important, and kernel density estimation is most suitable. By contrast, for longer lead times, GARCH models are more appropriate because the autocorrelation of the variance of the forecast errors is the most important deviation. In fact, even when no autocorrelation is present in the original demand, such autocorrelation can be present as a consequence of the overlapping process used to compute the lead time forecasts and the uncertainties arising in the estimation of the parameters of the forecasting model. Improvements are shown in terms of cycle service level, inventory investment and backorder volume. Simulations and real demand data from a manufacturer are used to illustrate our methodology.

  • 42.
    Trapero, Juan R.
    et al.
    Universidad de Castilla-La Mancha, Ciudad Real, Spain.
    Kourentzes, Nikolaos
    Lancaster University, Lancaster, United Kingdom.
    Fildes, Robert
    Lancaster University, Lancaster, United Kingdom.
    On the identification of sales forecasting models in the presence of promotions2015In: Journal of the Operational Research Society, ISSN 0160-5682, E-ISSN 1476-9360, Vol. 66, no 2, p. 299-307Article in journal (Refereed)
    Abstract [en]

    Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.

  • 43.
    Trapero, Juan R.
    et al.
    Universidad de Castilla-La Mancha, Departamento de Administracion de Empresas, Ciudad Real, Spain.
    Kourentzes, Nikolaos
    Lancaster University, Department of Management Science, United Kingdom.
    Martin, A.
    Universidad de Castilla-La Mancha, Departamento de Administracion de Empresas, Ciudad Real, Spain.
    Short-term solar irradiation forecasting based on Dynamic Harmonic Regression2015In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 84, p. 289-295Article in journal (Refereed)
    Abstract [en]

    Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1-24h) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24h ahead. 

  • 44.
    Trapero, Juan R.
    et al.
    Universidad de Castilla-La Mancha, Departamento de Administracion de Empresas, Ciudad Real, Spain.
    Pedregal, Diego. J.
    Universidad de Castilla-La Mancha, Departamento de Administracion de Empresas, Ciudad Real, Spain.
    Fildes, R.
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
    Analysis of judgmental adjustments in the presence of promotions2013In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 29, no 2, p. 234-243Article in journal (Refereed)
    Abstract [en]

    Sales forecasting is becoming increasingly complex, due to a range of factors, such as the shortening of product life cycles, increasingly competitive markets, and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers then add information to the forecast, such as future promotions, potentially improving the accuracy. Despite the importance of judgment and promotions, papers devoted to studying their relationship with forecasting performance are scarce. We analyze the accuracy of managerial adjustments in periods of promotions, based on weekly data from a manufacturing company. Intervention analysis is used to establish whether judgmental adjustments can be replaced by multivariate statistical models when responding to promotional information. We show that judgmental adjustments can enhance baseline forecasts during promotions, but not systematically. Transfer function models based on past promotions information achieved lower overall forecasting errors. Finally, a hybrid model illustrates the fact that human experts still added value to the transfer function models. 

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