ARIMA demand forecasting by aggregation
type: Dataset
description: Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce demand uncertainty and consequently improve the forecasting (and inventory control) performance. An intuitively appealing such approach that is known to be effective is demand aggregation. One approach is to aggregate demand in lower-frequency ‘time buckets’. Such an approach is often referred to, in the academic literature, as temporal aggregation. Another approach discussed in the literature is that associated with cross-sectional aggregation, which involves aggregating different time series to obtain higher level forecasts.This research discusses whether it is appropriate to use the original (not aggregated) data to generate a forecast or one should rather aggregate data first and then generate a forecast. This Ph.D. thesis reveals the conditions under which each approach leads to a superior performance as judged based on forecast accuracy. Throughout this work, it is assumed that the underlying structure of the demand time series follows an AutoRegressive Integrated Moving Average (ARIMA) process.In the first part of our1 research, the effect of temporal aggregation on demand forecasting is analysed. It is assumed that the non-aggregate demand follows an autoregressive moving average process of order one, ARMA(1,1). Additionally, the associated special cases of a first-order autoregressive process, AR(1) and a moving average process of order one, MA(1) are also considered, and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregate and the non-aggregate demand in order to contrast the relevant forecasting performances. The theoretical analysis is validated by an extensive numerical inve…
identifier: NNT: 2013BOR15228
issued: 2026-05-05T14:08:52.110416
language: en
modified: 2026-05-05T14:08:52.110421
contact point: Organization
  fn: CCSD
keyword: agregation-temporelle
keyword: agregation-transversale
keyword: cross-sectional-aggregation
keyword: demand-forecasting
keyword: infoeu-reposemanticsdoctoralthesis
keyword: lissage-exponentielle
keyword: nonstationary-processes
keyword: prevision-de-la-demande
keyword: processus-non-stationnaires
keyword: processus-stationnaires
keyword: single-exponential-smoothing
keyword: spiotherengineering-sciences-physicsother
keyword: stationary-processes
keyword: temporal-aggregation
keyword: theses
landing page: https://theses.hal.science/tel-00980614

ARIMA demand forecasting by aggregation
type: Distribution
format: HTML
issued: 2026-05-05T14:08:52.116479
modified: 2026-05-05T14:08:52.094201
access URL: https://theses.hal.science/tel-00980614

test_moissonnage_selune
type: Agent