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Kourentzes, NikolaosORCID iD iconorcid.org/0000-0003-0211-5218
Publications (10 of 51) Show all publications
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N. & Panagiotelis, A. (2024). Editorial: Innovations in hierarchical forecasting. International Journal of Forecasting
Open this publication in new window or tab >>Editorial: Innovations in hierarchical forecasting
2024 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200Article in journal (Other academic) Epub ahead of print
Place, publisher, year, edition, pages
Elsevier, 2024
National Category
Probability Theory and Statistics Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-23564 (URN)10.1016/j.ijforecast.2024.01.003 (DOI)2-s2.0-85183025435 (Scopus ID)
Note

Correspondence Address: G. Athanasopoulos; Monash University, Australia; email: george.athanasopoulos@monash.edu; CODEN: IJFOE

Available from: 2024-02-01 Created: 2024-02-01 Last updated: 2024-02-26Bibliographically approved
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N. & Panagiotelis, A. (2023). Forecast reconciliation: A review. International Journal of Forecasting
Open this publication in new window or tab >>Forecast reconciliation: A review
2023 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200Article, review/survey (Refereed) Epub ahead of print
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. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Aggregation, Coherence, Cross-temporal, Grouped time series, Hierarchical time series, Temporal aggregation
National Category
Probability Theory and Statistics Computational Mathematics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-23528 (URN)10.1016/j.ijforecast.2023.10.010 (DOI)2-s2.0-85181065888 (Scopus ID)
Funder
Australian Research Council, IC200100009
Note

CC BY 4.0 DEED

© 2023 The Author(s)

Available online 29 December 2023

Correspondence Address: G. Athanasopoulos; Monash University, VIC, 3145, Australia; email: george.athanasopoulos@monash.edu; CODEN: IJFOE

We thank Tommaso Di Fonzo, Xiaoqian Wang and Daniele Girolimetto for providing helpful comments onan earlier draft of this paper. Rob J Hyndman was funded by the Australian Government through the Australian Research Council Industrial Transformation Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Project ID IC200100009.

Available from: 2024-01-11 Created: 2024-01-11 Last updated: 2024-01-31Bibliographically approved
Ramos, P., Oliveira, J. M., Kourentzes, N. & Fildes, R. (2023). Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?. Applied System Innovation, 6(1), Article ID 3.
Open this publication in new window or tab >>Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
2023 (English)In: Applied System Innovation, ISSN 2571-5577, Vol. 6, no 1, article id 3Article in journal (Refereed) Published
Abstract [en]

Retailers depend on accurate forecasts of product sales at the Store × SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives. 

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
forecasting, principal components analysis, promotions, retailing, seasonality, shrinkage
National Category
Business Administration
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-22318 (URN)10.3390/asi6010003 (DOI)000938187300001 ()2-s2.0-85148702362 (Scopus ID)
Note

CC BY 4.0

© 2022 by the authors.

This research received no external funding.

Available from: 2023-03-09 Created: 2023-03-09 Last updated: 2023-05-04Bibliographically approved
Petropoulos, F., Kourentzes, N., Nikolopoulos, K. & Siemsen, E. (2023). Judgmental selection of forecasting models (reprint). In: Matthias Seifert (Ed.), Judgment in Predictive Analytics: (pp. 53-84). Cham: Springer, 343
Open this publication in new window or tab >>Judgmental selection of forecasting models (reprint)
2023 (English)In: 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.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
International Series in Operations Research & Management Science, ISSN 0884-8289, E-ISSN 2214-7934 ; 343
Keywords
Behavioral operations, Combination, Decomposition, Model selection, Industrial engineering, Operations research, Behavioral studies, Forecasting modeling, Information criterion, Standard algorithms, Structural component, Forecasting
National Category
Probability Theory and Statistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-22905 (URN)10.1007/978-3-031-30085-1_3 (DOI)2-s2.0-85162055731 (Scopus ID)978-3-031-30084-4 (ISBN)978-3-031-30087-5 (ISBN)978-3-031-30085-1 (ISBN)
Available from: 2023-06-30 Created: 2023-06-30 Last updated: 2023-07-14Bibliographically approved
Svetunkov, I., Kourentzes, N. & Killick, R. (2023). Multi-step estimators and shrinkage effect in time series models. Computational statistics (Zeitschrift)
Open this publication in new window or tab >>Multi-step estimators and shrinkage effect in time series models
2023 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658Article in journal (Refereed) Epub ahead of print
Abstract [en]

Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise parameters through a prediction error loss function. Estimation methods based on multiple steps ahead forecast errors have been shown to lead to more robust and less biased estimates of parameters. However, a plausible explanation of why this is the case is lacking. In this paper, we provide this explanation, showing that the main benefit of these estimators is in a shrinkage effect, happening in univariate models naturally. However, this can introduce a series of limitations, due to overly aggressive shrinkage. We discuss the predictive likelihoods related to the multistep estimators and demonstrate what their usage implies to time series models. To overcome the limitations of the existing multiple steps estimators, we propose the Geometric Trace Mean Squared Error, demonstrating its advantages. We conduct a simulation experiment showing how the estimators behave with different sample sizes and forecast horizons. Finally, we carry out an empirical evaluation on real data, demonstrating the performance and advantages of the estimators. Given that the underlying process to be modelled is often unknown, we conclude that the shrinkage achieved by the GTMSE is a competitive alternative to conventional ones.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
ARIMA, ETS, Multi-step estimators, Shrinkage, Time series analysis
National Category
Control Engineering
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-22982 (URN)10.1007/s00180-023-01377-x (DOI)001013065600001 ()2-s2.0-85162849847 (Scopus ID)
Note

CC BY 4.0

Published: 24 June 2023

Springer

Correspondence: Ivan Svetunkov; Department of Management Science, Lancaster University Management School, Lancaster, Lancashire, LA1 4YX, United Kingdom; email: i.svetunkov@lancaster.ac.uk

Available from: 2023-07-06 Created: 2023-07-06 Last updated: 2023-10-10Bibliographically approved
Athanasopoulos, G. & Kourentzes, N. (2023). On the evaluation of hierarchical forecasts. International Journal of Forecasting, 39(4), 1502-1511
Open this publication in new window or tab >>On the evaluation of hierarchical forecasts
2023 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 39, no 4, p. 1502-1511Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Aggregation, Coherence, Hierarchical time series, Multiple objectives, Reconciliation
National Category
Probability Theory and Statistics Computer Sciences Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-22015 (URN)10.1016/j.ijforecast.2022.08.003 (DOI)001072979600001 ()2-s2.0-85140264407 (Scopus ID)
Note

© 2022 International Institute of Forecasters

Available from: 2022-11-03 Created: 2022-11-03 Last updated: 2023-10-13Bibliographically approved
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N. & O'Hara-Wild, M. (2023). Probabilistic Forecasts Using Expert Judgment: The Road to Recovery From COVID-19. Journal of Travel Research, 62(1), 233-258, Article ID 00472875211059240.
Open this publication in new window or tab >>Probabilistic Forecasts Using Expert Judgment: The Road to Recovery From COVID-19
2023 (English)In: Journal of Travel Research, ISSN 0047-2875, E-ISSN 1552-6763, Vol. 62, no 1, p. 233-258, article id 00472875211059240Article in journal (Refereed) Published
Abstract [en]

The COVID-19 pandemic has had a devastating effect on many industries around the world including tourism and policy makers are interested in mapping out what the recovery path will look like. We propose a novel statistical methodology for generating scenario-based probabilistic forecasts based on a large survey of 443 tourism experts and stakeholders. The scenarios map out pessimistic, most-likely and optimistic paths to recovery. Taking advantage of the natural aggregation structure of tourism data due to geographic locations and purposes of travel, we propose combining forecast reconciliation and forecast combinations implemented to historical data to generate robust COVID-free counterfactual forecasts, to contrast against. Our empirical application focuses on Australia, analyzing international arrivals and domestic flows. Both sectors have been severely affected by travel restrictions in the form of international and interstate border closures and regional lockdowns. The two sets of forecasts, allow policy makers to map out the road to recovery and also estimate the expected effect of the pandemic.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
forecasting, judgmental, probabilistic, scenarios, survey
National Category
Social Sciences Interdisciplinary
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-20922 (URN)10.1177/00472875211059240 (DOI)000751413200001 ()2-s2.0-85124149600 (Scopus ID)
Note

© The Author(s) 2022

Corresponding Author: George Athanasopoulos, Monash University, 900 Dandenong Road, Caulfield East, VIC 3145, Australia. Email: George.Athanasopoulos@monash

Article first published online: January 27, 2022

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Available from: 2022-02-17 Created: 2022-02-17 Last updated: 2022-11-18Bibliographically approved
Pritularga, K. F., Svetunkov, I. & Kourentzes, N. (2023). Shrinkage estimator for exponential smoothing models. International Journal of Forecasting, 39(3), 1351-1365
Open this publication in new window or tab >>Shrinkage estimator for exponential smoothing models
2023 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 39, no 3, p. 1351-1365Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
42, ETS, Forecasting, Parameter estimation, Regularisation, State-space model
National Category
Probability Theory and Statistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-21775 (URN)10.1016/j.ijforecast.2022.07.005 (DOI)001032899600001 ()2-s2.0-85136756043 (Scopus ID)
Note

© 2022 International Institute of Forecasters

Available online 12 August 2022

Correspondence to: Department of Management Science, Lancaster University Management School, Lancaster, Lancashire, LA1 4YX, UK. E-mail address: k.pritularga@lancaster.ac.uk (K.F. Pritularga).

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Available from: 2022-09-08 Created: 2022-09-08 Last updated: 2023-08-14Bibliographically approved
Sroginis, A., Fildes, R. & Kourentzes, N. (2023). Use of contextual and model-based information in adjusting promotional forecasts. European Journal of Operational Research, 307(3), 1177-1191
Open this publication in new window or tab >>Use of contextual and model-based information in adjusting promotional forecasts
2023 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 307, no 3, p. 1177-1191Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Artificial intelligence, Decision support systems, Information use, Statistics, Supply chains, Behavioral OR, Business demands, Business forecasting, Case-studies, Demand planning, Human judgments, Judgmental forecasting, Model-based OPC, Promotion, Statistical forecasting, Forecasting, Behavioural OR, Promotions
National Category
Probability Theory and Statistics Reliability and Maintenance
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-22035 (URN)10.1016/j.ejor.2022.10.005 (DOI)000965072900001 ()2-s2.0-85140736978 (Scopus ID)
Note

CC BY 4.0

Attribution 4.0 International (CC BY 4.0)

© 2022 The Authors

Available online 8 October 2022

Available from: 2022-11-10 Created: 2022-11-10 Last updated: 2023-05-02Bibliographically approved
Saoud, P., Kourentzes, N. & Boylan, J. E. (2022). Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation. Omega: The International Journal of Management Science, 110, Article ID 102614.
Open this publication in new window or tab >>Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation
2022 (English)In: Omega: The International Journal of Management Science, ISSN 0305-0483, E-ISSN 1873-5274, Vol. 110, article id 102614Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Demand uncertainty, Forecast errors correlations, Forecasting, Lead time demand variance, Safety stock
National Category
Probability Theory and Statistics Transport Systems and Logistics Economics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-20986 (URN)10.1016/j.omega.2022.102614 (DOI)000806376500011 ()2-s2.0-85125853141 (Scopus ID)
Note

© 2022 Elsevier Ltd

Available from: 2022-03-17 Created: 2022-03-17 Last updated: 2022-06-23Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0211-5218

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