Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry

Abstract This article compares four non-linear forecasting methods: multiplicative seasonal ARIMA, unobserved components (UC), wavelet-based and support vector machines (SVM). Whereas the first two methods are well known in the time series field, the other two rely on recently developed mathematical techniques. Based on forecasting accuracy and encompassing tests applied to shipments data of the U.S. metal and material manufacturing industry for 1958–2000, we conclude that that these two novel forecast techniques can either outperform the traditional ones or provide them with extra forecast information. In particular, based on the Granger–Newbold test, it appears that wavelets may be a promising new technique.

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