Adding EMD Process and Filtering Analysis to Enhance Performances of ARIMA Model When Time Series Is Measurement Data

Feng-Jenq Lin ()
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Feng-Jenq Lin: Department of Applied Economics and Management, National I-Lan University, Yilan,Taiwan.

Journal for Economic Forecasting, 2015, issue 2, 92-104

Abstract: In this paper, one process that integratesthe Empirical Mode Decomposition with filtering analysis was proposed to reconstruct the de-noise data series when the original is measurement data. The ARIMA model was augmented with the above process (here from referred to as EF-ARIMA) to treat de-noise measurement data. Model fit and forecasting performance of EF-ARIMA, using de-noise data set, were compared to those of the traditional ARIMA, which used the original data set, in an empirical study. By examining the MAE, MAPE, RMSE and Theil's inequality coefficients, it was concluded that EF-ARIMA outperformed its traditional counterpart. It also shows that the proposed hybrid forecasting approach is feasible and reliable. The results suggest application implications for forecasting measurement data sets in other areas as well.

Keywords: Hilbert-Huang transform; empirical mode decomposition; filtering analysis; measurement data; ARIMA model (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2015
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