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Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting

Sādhanā volume 41, pages 695–706 (2016)Cite this article

Abstract

Time series data (TSD) originating from different applications have dissimilar characteristics. Hence for prediction of TSD, diversified varieties of prediction models exist. In many applications, hybrid models provide more accurate predictions than individual models. One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ARIMA–ANN model is proposed, which combines the concepts of the recently developed moving average (MA) filter based hybrid ARIMA–ANN model, with a processing technique involving a partitioning–interpolation (PI) step. The improved prediction accuracy of the proposed PI based hybrid ARIMA–ANN model is justified using a simulation experiment. Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all the others, and hence is a promising model for TSD prediction.

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Authors and Affiliations

  1. M S Ramaiah University of Applied Sciences, Bangalore, 560058, India

    C Narendra Babu & Pallaviram Sure

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  1. C Narendra Babu
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  2. Pallaviram Sure
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Correspondence to C Narendra Babu.

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Babu, C.N., Sure, P. Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting. Sādhanā 41, 695–706 (2016). https://doi.org/10.1007/s12046-016-0508-5

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  • DOI: https://doi.org/10.1007/s12046-016-0508-5

Keywords

  • Time series forecasting
  • ARIMA
  • ANN
  • partitioning and interpolation
  • Box–Jenkins methodology