A Comparative Analysis of Short Time Series Processing Methods

Abstract

This article analyzes the traditional time series processing methods that are used to perform the task of short time series analysis in demand forecasting. The main aim of this paper is to scrutinize the ability of these methods to be used when analyzing short time series. The analyzed methods include exponential smoothing, exponential smoothing with the development trend and moving average method. The paper gives the description of the structure and main operating principles. The experimental studies are conducted using real demand data. The obtained results are analyzed; and the recommendations are given about the use of these methods for short time series analysis.