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Forecasting of curves using a Kohonen classification
Author(s) -
Cottrell Marie,
Girard Bernard,
Rousset Patrick
Publication year - 1998
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(1998090)17:5/6<429::aid-for706>3.0.co;2-h
Subject(s) - self organizing map , artificial intelligence , computer science , pattern recognition (psychology) , econometrics , mathematics , artificial neural network
This paper addresses an extensively studied problem, which is a particular case of long‐term forecasting. In many practical situations, one has to predict a complete curve, i.e. the set of the 24 hourly values for the next day, of all the daily values for the next month or for the next year. For example, it is the case if the matter is to forecast the daily half‐hour electricity consumption. Many methods have been developed, standard linear methods (e.g. ARIMA) as well as neural ones. In this paper we present a very simple method that we call the K‐method. We assume the forecasting problem can be split up into three subproblems: the forecast of the mean (level of the values), of the standard deviation (scattering) and of the normalized profile (which essentially represents the shape). The profiles are classified using a Kohonen map with the neighbourhood preservation property and the mean and variance are fitted using any convenient short‐term forecasting method. Then, for some future curve, a strategy is defined in order to compute its expected normalized profile, the mean and the variance are forecast and the expected curve is computed. This method is low computation time consuming and is easy to develop. Two applications are presented: an example using artificial data and the prediction of the daily half‐hour electrical power curves in France. Copyright © 1998 John Wiley & Sons, Ltd.