Simple versus complex selection rules for forecasting many time series

Simple versus complex selection rules for forecasting many time series

A major problem for many organisational forecasters is to choose the appropriate forecasting method for a large number of time series. Various selection rules have been proposed in order to enhance forecasting accuracy. The simpler approach for model selection involves the identification of a single method, which is applied to all data series in an aggregate manner, without taking into account ...

Simple versus complex forecasting: The evidence

This article introduces the Special Issue on simple versus complex methods in forecasting. Simplicity in forecasting requires that (1) method, (2) representation of cumulative knowledge, (3) relationships in models, and (4) relationships among models, forecasts, and decisions are all sufficiently uncomplicated as to be easily understood by decision-makers. Our review of studies comparing simple...

An evaluation of simple forecasting model selection rules

A major problem for many organisational forecasters is to choose the appropriate forecasting method for a large number of data series. Model selection aims to identify the best method of forecasting for an individual series within the data set. Various selection rules have been proposed in order to enhance forecasting accuracy. In theory, model selection is appealing, as no single extrapolation...

Model selection for weakly dependent time series forecasting

Empirical information criteria for time series forecasting model selection

In this paper, we propose a new Empirical Information Criterion (EIC) for model selection which penalizes the likelihood of the data by a function of the number of parameters in the model. It is designed to be used where there are a large number of time series to be forecast. However, a bootstrap version of the EIC can be used where there is a single time series to be forecast. The EIC provides...