Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?

Kourentzes, Nikolaos; Rostami-Tabar, Bahman; Barrow, Devon K.

doi:10.1016/j.jbusres.2017.04.016

Cited by 44 publications

(19 citation statements)

References 37 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improvements have been reported both for short and long term forecast horizons, across different applications (Kourentzes and Petropoulos, 2016;Barrow and Kourentzes, 2018). Kourentzes et al (2017) showed that although MAPA is not optimal at any aggregation level, it still provides more accurate forecasts than conventional approaches to temporal aggregation, as it is very resistant to any modeling misspecification.…”

Section: Multiple Temporal Aggregation For Forecastingmentioning

confidence: 98%

Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption

et al. 2020

Self Cite

View full text Add to dashboard Cite

Achieving high accuracy in load forecasting requires the selection of appropriate forecasting models, able to capture the special characteristics of energy consumption time series. When hierarchies of load from different sources are considered together, the complexity increases further; for example, when forecasting both at 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 forecast can address the aggregation consistency concerns, it does not resolve the model selection uncertainty. To address this we rely on Multiple Temporal Aggregation, which has been shown to mitigate the model selection problem for low frequency time series. We propose a modification for high frequency time series and combine conventional cross-sectional hierarchical forecasting with multiple temporal aggregation. The effect of incorporating temporal aggregation in hierarchical forecasting is empirically assessed using a real data set from five bank branches, demonstrating superior accuracy, aggregation consistency and reliable automatic forecasting.

show abstract

Section: Multiple Temporal Aggregation For Forecastingmentioning

confidence: 98%

Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Secondly, the performance of aggregation was generally found to improve as the aggregation level increases. Kourentzes et al (2017) contrasted the effectiveness of using a multiple aggregation level or a single optimal aggregation level in forecast accuracy improvement. They conclude that using TA for demand forecasting is beneficial and argue that further research in identifying the optimal aggregation level is required.…”

Section: Research Backgroundmentioning

confidence: 99%

“…For example, budget forecasts are not required at the weekly horizon decision that is typical of inventory management, but they are needed at much longer horizons (Lapide, 2004). Recent advances have shown the benefits associated with TA in terms of forecast accuracy and stock control improvements when non-optimal forecasting methods are used (Babai et al, 2012;Kourentzes et al, 2017;Rostami-Tabar et al, 2014). However, it should be noted that the benefit of this approach has been shown in the literature only under single exponential smoothing, which is optimal (minimum Mean Square Error) for an ARIMA(0,1,1) process.…”

Section: Introductionmentioning

confidence: 99%

The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes

Rostami-Tabar

Babaï

Ali

et al. 2019

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

Various approaches have been considered in the literature to improve demand forecasting in supply chains. Among these approaches, non-overlapping temporal aggregation has been shown to be an effective approach that can improve forecast accuracy. However, the benefit of this approach has been shown only under single exponential smoothing (when it is a non-optimal method) and no theoretical analysis has been conducted to look at the impact of this approach under optimal forecasting. This paper aims to bridge this gap by analysing the impact of temporal aggregation on supply chain demand and orders when optimal forecasting is used. To do so, we consider a two-stage supply chain (e.g. a retailer and a manufacturer) where the retailer faces an autoregressive moving average demand process of order (1,1)-ARMA(1,1)-that is forecasted by using the optimal Minimum Mean Squared Error (MMSE) forecasting method. We derive the analytical expressions of the mean squared forecast error (MSE) at the retailer and the manufacturer levels as well as the bullwhip ratio when the aggregation approach is used. We numerically show that, although the aggregation approach leads to an accuracy loss at the retailer's level, it may result in a reduction of the MSE at the manufacturer level up to 90% and a reduction of the bullwhip effect in the supply chain that can reach up to 84% for high leadtimes.

show abstract

“…Advantages and disadvantages of a use of different levels of time aggregation of data, their combinations, and a use of a time hierarchy for forecasting have been analyzed in modern studies. It has been proven in paper [6] that an application of one optimal level of time aggregation leads to a decrease in the accuracy of the obtained model, and an application of several levels of time aggregation is reliable for modeling of uncertainty, but is not optimal by parameters. Study [7] suggests a use of a time hierarchy to obtain forecasts of time series.…”

Section: Fig 1 Structure Of Classic Marketing Information Managemenmentioning

confidence: 99%

Development of the subsystem of forecasting for the system of marketing information management at an industrial enterprise

Oklander

Pedko

et al. 2017

EEJET

View full text Add to dashboard Cite

Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?

Cited by 44 publications

References 37 publications

Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption

Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption

The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes

Development of the subsystem of forecasting for the system of marketing information management at an industrial enterprise

Contact Info

Product

Resources

About