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Kourentzes, NikolaosORCID iD iconorcid.org/0000-0003-0211-5218
Publications (10 of 35) Show all publications
Spiliotis, E., Petropoulos, F., Kourentzes, N. & Assimakopoulos, V. (2020). Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. Applied Energy, 261, Article ID 114339.
Open this publication in new window or tab >>Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption
2020 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 261, article id 114339Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
electricity consumption, exponential smoothing, Hierarchical forecasting, Seasonality shrinkage, Temporal aggregation, Electric power utilization, Energy utilization, Shrinkage, Time series, Electricity-consumption, High frequency time series, Individual characteristics, Low frequency time series, Model selection problem, Seasonality, Forecasting, electricity, energy use, forecasting method, hierarchical system, smoothing, temporal analysis, time series analysis
National Category
Energy Systems Probability Theory and Statistics Energy Engineering
Identifiers
urn:nbn:se:his:diva-18231 (URN)10.1016/j.apenergy.2019.114339 (DOI)2-s2.0-85076830755 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
Kourentzes, N. & Athanasopoulos, G. (2020). Elucidate structure in intermittent demand series. European Journal of Operational Research
Open this publication in new window or tab >>Elucidate structure in intermittent demand series
2020 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860Article in journal (Refereed) Accepted
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.

Keywords
Forecasting, temporal aggregation, temporal hierarchies, forecast combination, forecast reconciliation
National Category
Probability Theory and Statistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18457 (URN)10.1016/j.ejor.2020.05.046 (DOI)
Available from: 2020-05-22 Created: 2020-05-22 Last updated: 2020-06-01
Kourentzes, N., Trapero, J. R. & Barrow, D. K. (2020). Optimising forecasting models for inventory planning. International Journal of Production Economics, 225, Article ID 107597.
Open this publication in new window or tab >>Optimising forecasting models for inventory planning
2020 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 225, article id 107597Article in journal (Refereed) Published
Abstract [en]

Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets. The forecasting literature, often disjoint from the needs of the forecast users, has focused on providing optimal models in terms of likelihood and various accuracy metrics. However, there is evidence that this does not always lead to better inventory performance, as often the translation between forecast errors and inventory results is not linear. In this study, we consider an approach to parametrising forecasting models by directly considering appropriate inventory metrics and the current inventory policy. We propose a way to combine the competing multiple inventory objectives, i.e. meeting demand, while eliminating excessive stock, and use the resulting cost function to identify inventory optimal parameters for forecasting models. We evaluate the proposed parametrisation against established alternatives and demonstrate its performance on real data. Furthermore, we explore the connection between forecast accuracy and inventory performance and discuss the extent to which the former is an appropriate proxy of the latter. 

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Forecasting, Inventory management, Likelihood, Optimisation, Simulation, Cost functions, Inventory control, Forecasting models, Inventory performance, Inventory planning, Inventory policies, Optimisations
National Category
Economics Transport Systems and Logistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18166 (URN)10.1016/j.ijpe.2019.107597 (DOI)000532795300019 ()2-s2.0-85077690632 (Scopus ID)
Available from: 2020-01-24 Created: 2020-01-24 Last updated: 2020-05-29Bibliographically approved
Kourentzes, N., Barrow, D. & Petropoulos, F. (2019). Another look at forecast selection and combination: Evidence from forecast pooling. International Journal of Production Economics, 209, 226-235
Open this publication in new window or tab >>Another look at forecast selection and combination: Evidence from forecast pooling
2019 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 209, p. 226-235Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Cross-validation, Forecast combination, Forecast pooling, Forecasting, Model selection, Lakes, Uncertainty analysis, Cross validation, Forecast accuracy, Forecast combinations, Forecast errors, Modelling process, Performance criterion, Weighting scheme
National Category
Probability Theory and Statistics Other Electrical Engineering, Electronic Engineering, Information Engineering Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:his:diva-18236 (URN)10.1016/j.ijpe.2018.05.019 (DOI)000464087900023 ()2-s2.0-85047273712 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
Kourentzes, N. & Athanasopoulos, G. (2019). Cross-temporal coherent forecasts for Australian tourism. Annals of Tourism Research, 75, 393-409
Open this publication in new window or tab >>Cross-temporal coherent forecasts for Australian tourism
2019 (English)In: Annals of Tourism Research, ISSN 0160-7383, E-ISSN 1873-7722, Vol. 75, p. 393-409Article in journal (Refereed) Published
Abstract [en]

Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be ‘coherent’ that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecast, a special selection of research in this field.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Cross-sectional aggregation, Forecast combinations, Spatial correlations, Temporal aggregation, correlation, decision making, forecasting method, policy development, policy making, strategic approach, tourism management, Australia
National Category
Environmental Sciences Social Sciences Interdisciplinary
Identifiers
urn:nbn:se:his:diva-18234 (URN)10.1016/j.annals.2019.02.001 (DOI)000474679500029 ()2-s2.0-85062600988 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
Schaer, O., Kourentzes, N. & Fildes, R. (2019). Demand forecasting with user-generated online information. International Journal of Forecasting, 35(1), 197-212
Open this publication in new window or tab >>Demand forecasting with user-generated online information
2019 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 35, no 1, p. 197-212Article in journal (Refereed) Published
Abstract [en]

Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Electronic word-of-mouth, Google trends, Leading indicators, Product life-cycle, Search traffic, Social media
National Category
Other Mechanical Engineering Other Civil Engineering
Identifiers
urn:nbn:se:his:diva-18239 (URN)10.1016/j.ijforecast.2018.03.005 (DOI)000454976000014 ()2-s2.0-85047984721 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
Trapero, J. R., Cardós, M. & Kourentzes, N. (2019). Empirical safety stock estimation based on kernel and GARCH models. Omega: The International Journal of Management Science, 84, 199-211
Open this publication in new window or tab >>Empirical safety stock estimation based on kernel and GARCH models
2019 (English)In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 84, p. 199-211Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Forecasting, GARCH, Kernel density estimation, Prediction intervals, Risk, Safety stock, Supply chain, Volatility, article, error, human, investment, prediction, simulation, uncertainty
National Category
Economics Probability Theory and Statistics Transport Systems and Logistics
Identifiers
urn:nbn:se:his:diva-18233 (URN)10.1016/j.omega.2018.05.004 (DOI)000456760200014 ()2-s2.0-85048809757 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
Sagaert, Y. R., Kourentzes, N., De Vuyst, S., Aghezzaf, E.-H. & Desmet, B. (2019). Incorporating macroeconomic leading indicators in tactical capacity planning. International Journal of Production Economics, 209, 12-19
Open this publication in new window or tab >>Incorporating macroeconomic leading indicators in tactical capacity planning
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2019 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 209, p. 12-19Article in journal (Refereed) Published
Abstract [en]

Tactical capacity planning relies on future estimates of demand for the mid- to long-term. On these forecast horizons there is increased uncertainty that the analysts face. To this purpose, we incorporate macroeconomic variables into microeconomic demand forecasting. Forecast accuracy metrics, which are typically used to assess improvements in predictions, are proxies of the real decision associated costs. However, measuring the direct impact on decisions is preferable. In this paper, we examine the capacity planning decision at plant level of a manufacturer. Through an inventory simulation setup, we evaluate the gains of incorporating external macroeconomic information in the forecasts, directly, in terms of achieving target service levels and inventory performance. Furthermore, we provide an approach to indicate capacity alerts, which can serve as input for global capacity pooling decisions. Our work has two main contributions. First, we demonstrate the added value of leading indicator information in forecasting models, when evaluated directly on capacity planning. Second, we provide additional evidence that traditional metrics of forecast accuracy exhibit weak connection with the real decision costs, in particular for capacity planning. We propose a more realistic assessment of the forecast quality by evaluating both the first and second moment of the forecast distribution. We discuss implications for practice, in particular given the typical over-reliance on forecast accuracy metrics for choosing the appropriate forecasting model. 

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Capacity planning, Forecasting, Inventory, Leading indicators, Industrial economics, Industrial engineering, Forecast distribution, Forecasting modeling, Inventory performance, Macroeconomic variables, Tactical capacity planning
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:his:diva-18235 (URN)10.1016/j.ijpe.2018.06.016 (DOI)000464087900003 ()2-s2.0-85049326644 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
Trapero, J. R., Cardós, M. & Kourentzes, N. (2019). Quantile forecast optimal combination to enhance safety stock estimation. International Journal of Forecasting, 35(1), 239-250
Open this publication in new window or tab >>Quantile forecast optimal combination to enhance safety stock estimation
2019 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 35, no 1, p. 239-250Article in journal (Refereed) Published
Abstract [en]

The safety stock calculation requires a measure of the forecast error uncertainty. Such errors are usually assumed to be Gaussian iid (independently and identically distributed). However, deviations from iid lead to a deterioration in the performance of the supply chain. Recent research has shown that, contrary to theoretical approaches, empirical techniques that do not rely on the aforementioned assumptions can enhance the calculation of safety stocks. In particular, GARCH models cope with time-varying heterocedastic forecast error, and kernel density estimation does not need to rely on a determined distribution. However, if the forecast errors are time-varying heterocedastic and do not follow a determined distribution, the previous approaches are inadequate. We overcome this by proposing an optimal combination of the empirical methods that minimizes the asymmetric piecewise linear loss function, also known as the tick loss. The results show that combining quantile forecasts yields safety stocks with a lower cost. The methodology is illustrated with simulations and real data experiments for different lead times.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Combination, GARCH, Kernel density estimation, Quantile forecasting, Risk, Safety stock, Supply chain, Tick loss
National Category
Transport Systems and Logistics Economics
Identifiers
urn:nbn:se:his:diva-18238 (URN)10.1016/j.ijforecast.2018.05.009 (DOI)000454976000017 ()2-s2.0-85052327482 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
Kourentzes, N., Li, D. & Strauss, A. K. (2019). Unconstraining methods for revenue management systems under small demand. Journal of Revenue and Pricing Management, 18(1), 27-41
Open this publication in new window or tab >>Unconstraining methods for revenue management systems under small demand
2019 (English)In: Journal of Revenue and Pricing Management, ISSN 1476-6930, E-ISSN 1477-657X, Vol. 18, no 1, p. 27-41Article in journal (Refereed) Published
Abstract [en]

Sales data often only represent a part of the demand for a service product owing to constraints such as capacity or booking limits. Unconstraining methods are concerned with estimating the true demand from such constrained sales data. This paper addresses the frequently encountered situation of observing only a few sales events at the individual product level and proposes variants of small demand forecasting methods to be used for unconstraining. The usual procedure is to aggregate data; however, in that case we lose information on when restrictions were imposed or lifted within a given booking profile. Our proposed methods exploit this information and are able to approximate convex, concave or homogeneous booking curves. Furthermore, they are numerically robust due to our proposed group-based parameter optimization. Empirical results on accuracy and revenue performance based on data from a major car rental company indicate revenue improvements over a best practice benchmark by statistically significant 0.5–1.4% in typical scenarios.

Place, publisher, year, edition, pages
Palgrave Macmillan, 2019
Keywords
Demand unconstraining, Forecasting, Revenue management, Small demand
National Category
Transport Systems and Logistics Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:his:diva-18237 (URN)10.1057/s41272-017-0117-x (DOI)000464760100003 ()2-s2.0-85029810272 (Scopus ID)
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-03-02Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0211-5218

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