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

Author

Listed:
  • Feng-Jenq LIN

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

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.

Suggested Citation

  • Feng-Jenq LIN, 2015. "Adding EMD Process and Filtering Analysis to Enhance Performances of ARIMA Model When Time Series Is Measurement Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 92-104, June.
    • Handle: RePEc:rjr:romjef:v::y:2015:i:2:p:92-104
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    References listed on IDEAS

    as
    1. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    More about this item

    Keywords

    Hilbert-Huang transform; empirical mode decomposition; filtering analysis; measurement data; ARIMA model;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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