An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data

Prateek Pandey, Shishir Kumar and Sandeep Shrivastava
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Prateek Pandey: Jaypee University of Engineering and Technology, Guna, India
Shishir Kumar: Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, India
Sandeep Shrivastava: Jaypee University of Engineering and Technology, Guna, India

International Journal of Fuzzy System Applications (IJFSA), 2017, vol. 6, issue 4, 83-98

Abstract: In recent years, there has been a growing interest in Time Series forecasting. A number of time series forecasting methods have been proposed by various researchers. However, a common trend found in these methods is that they all underperform on a data set that exhibit uneven ups and downs (turbulences). In this paper, a new method based on fuzzy time-series (henceforth FTS) to forecast on the fundament of turbulences in the data set is proposed. The results show that the turbulence based fuzzy time series forecasting is effective, especially, when the available data indicate a high degree of instability. A few benchmark FTS methods are identified from the literature, their limitations and gaps are discussed and it is observed that the proposed method successfully overcome their deficiencies to produce better results. In order to validate the proposed model, a performance comparison with various conventional time series models is also presented.

Date: 2017
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