Welcome to the InfoSci Platform
Reference Hub6
An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data

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

Prateek Pandey, Shishir Kumar, Sandeep Shrivastava
Copyright: © 2017 |Volume: 6 |Issue: 4 |Pages: 16
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781522514961|DOI: 10.4018/IJFSA.2017100106
Cite Article Cite Article

MLA

Pandey, Prateek, et al. "An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data." IJFSA vol.6, no.4 2017: pp.83-98. http://doi.org/10.4018/IJFSA.2017100106

APA

Pandey, P., Kumar, S., & Shrivastava, S. (2017). An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data. International Journal of Fuzzy System Applications (IJFSA), 6(4), 83-98. http://doi.org/10.4018/IJFSA.2017100106

Chicago

Pandey, Prateek, Shishir Kumar, and Sandeep Shrivastava. "An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data," International Journal of Fuzzy System Applications (IJFSA) 6, no.4: 83-98. http://doi.org/10.4018/IJFSA.2017100106

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.