Non-stationary demand forecasting by cross-sectional aggregation

Rostami-Tabar, Bahman; Babaï, Mohamed Zied; Ducq, Yves; Syntetos, Aris A.

doi:10.1016/j.ijpe.2015.10.001

Cited by 20 publications

(15 citation statements)

References 46 publications

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“…The results indicate that the models' performances differ greatly across the hierarchy and depends on the forecasting horizon and the level of the hierarchy [26]. In general, consistent with the literature we found that models accuracy is higher at the top level due to the aggregation [25]. Note that the ML models use the MO approach.…”

Section: Resultssupporting

confidence: 87%

Machine learning applications in time series hierarchical forecasting

Abolghasemi,

Hyndman,

Tarr

et al. 2019

Preprint

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Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a decision. Top-Down (TD), Bottom-Up (BU) and Optimal Combination (COM) are common HF models. These approaches are static and often ignore the dynamics of the series while disaggregating them. Consequently, they may fail to perform well if the investigated group of time series are subject to large changes such as during the periods of promotional sales. We address the HF problem of predicting real-world sales time series that are highly impacted by promotion. We use three machine learning (ML) models to capture sales variations over time. Artificial neural networks (ANN), extreme gradient boosting (XGboost), and support vector regression (SVR) algorithms are used to estimate the proportions of lower-level time series from the upper level. We perform an in-depth analysis of 61 groups of time series with different volatilities and show that ML models are competitive and outperform some well-established models in the literature.

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Section: Resultssupporting

confidence: 87%

Machine learning applications in time series hierarchical forecasting

Abolghasemi,

Hyndman,

Tarr

et al. 2019

Preprint

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“…An alternative line of attack is to take a contingent approach and analyse the conditions under which one method produces more accurate forecasts than another (e.g., Shlifer and Wolff, 1979;Lutkepohl, 1984;Viswanathan et al, 200;Rostami-Tabar et al, 2015). Widiarta et al (2008) evaluated top-down versus bottom-up forecasting in a production planning context for the purpose of estimating SKU level requirements.…”

Section: Hierarchical Product Aggregationmentioning

confidence: 99%

“…More recently, Rostami-Tabar et al (2015) analysed theoretically and by means of simulation on theoretically generated data the relative performance of top-down and bottom-up for forecasting both aggregate and SKU level demand. The latter was assumed to follow a non-stationary ARIMA (0,1,1) demand process and exponential smoothing (which is optimal for the process under concern) was assumed to be employed for forecasting purposes.…”

Section: Hierarchical Product Aggregationmentioning

confidence: 99%

Supply chain forecasting: Theory, practice, their gap and the future

Syntetos

Babai

Boylan

et al. 2016

European Journal of Operational Research

234

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HIGHLIGHTSThe literature on supply chain forecasting is critically reviewed;The process of involving the forecasting community towards that task is described;Gaps between theory and practice are identified; Data and software related issues are explicitly considered; Challenges are summarised followed by suggestions for further research. ABSTRACTSupply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products).This paper provides a comprehensive review of the literature and aims at bridging the gap between the theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper.

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“…Previous studies have utilized different approaches to forecast the prices of underlying assets, for instance ordinary least squares (OLS) (Aye et al 2015;Birkelund et al 2015;Botterud, Kristiansen, and Ilic 2010;Danese and Kalchschmidt 2011;Van Donselaar et al 2016;Haugom et al 2011;Junttila, Myllymäki, and Raatikainen 2018;Mosquera-López and Nursimulu 2019;Weron and Zator 2014), the error correction model and cointegration (Eksoz, Mansouri, and Bourlakis 2014;Fantazzini and (Bunn and Chen 2013;Girish, Rath, and Akram 2018;Junttila et al 2018;Nakajima and Hamori 2013;Park, Mjelde, and Bessler 2006), the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) type (Bowden and Payne 2008;Charwand, Gitizadeh, and Siano 2017;Ferbar Tratar 2015;Furió and Chuliá 2012;Loi and Jindal 2019;Rostami-Tabar et al 2015;Tratar, Mojškerc, and Toman 2016), machine learning approaches (Lolli et al 2017;Nikolopoulos, Babai, and Bozos 2016;Tang and Rehme 2017;Y. Zhu et al 2019), optimization and networks (Hasni et al 2019;Le, Ilea, and Bovo 2019;Mirza and Bergland 2012;Tande 2003;Zhu, Mukhopadhyay, and Yue 2011), quantile smoothing (Bruzda 2019), and generalized additive models (Serinaldi 2011).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Forecasting Models in an Electricity Market under Volatility

et al. 2021

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Short-term electricity price forecasting has received considerable attention in recent years. Despite this increased interest, the literature lacks a concrete consensus on the most suitable forecasting approach. This study reports an extensive empirical analysis that we conducted to evaluate the short-term price forecasting dynamics of different regions in the Swedish electricity market (SEM). We utilized several forecasting approaches ranging from standard conditional volatility models to wavelet-based forecasting. In addition, we performed out-of-sample forecasting and back-testing, and we evaluated the performance of these models. Our empirical analysis indicates that an ARMA-GARCH framework with the Student's t-distribution significantly outperforms other frameworks. We only performed wavelet-based forecasting based on the MAPE. The results of the robust forecasting methods are capable of displaying the importance of proper forecasting process design, policy implications for market efficiency, and predictability in the SEM.

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Non-stationary demand forecasting by cross-sectional aggregation

Cited by 20 publications

References 46 publications

Machine learning applications in time series hierarchical forecasting

Machine learning applications in time series hierarchical forecasting

Supply chain forecasting: Theory, practice, their gap and the future

Analysis of Forecasting Models in an Electricity Market under Volatility

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