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EWMA Based Fusion for Time Series Forecasting  

Shin, Hyung Won (Department of Computer Science & Industrial Systems Engineering 134, Yonsei University)
Sohn, So Young (Department of Computer Science & Industrial Systems Engineering 134, Yonsei University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.28, no.2, 2002 , pp. 171-177 More about this Journal
Abstract
In this paper, we propose a new data fusion method to improve the performance of individual prediction models for time series data. Individual models used are ARIMA and neural network and their results are combined based on the weight reflecting the inverse of EWMA of squared prediction error of each individual model. Monte Carlo simulation is used to identify the situation where the proposed approach can take a vintage point over typical fusion methods which utilize MSE for weight. Study results indicate the following: EWMA performs better than MSE fusion when the data size is large with a relatively big amplitude, which is often observed in intra-cranial pressure data. Additionally, EWMA turns out to be a best choice among MSE fusion and the two individual prediction models when the data size is large with relatively small random noises, often appearing in tax revenue data.
Keywords
EWMA; fusion; time series; ARIMA; neural network;
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