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A Novel Hybrid Method for Time Series Forecasting Using Soft Computing Approach

  • Arpita SanghaniEmail author
  • Nirav Bhatt
  • N. C. Chauhan
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Improving the forecasting accuracy of time series is important and has always been a challenging research domain. From many decades, Auto-Regressive Integrated Moving Average (ARIMA) has been popularly used for statistic forecasting however it will solely forecast linear half accurately because it cannot capture the nonlinear patterns. Therefore here, we have projected a hybrid model of ARIMA and SVM. As Support Vector Machine (SVM) has demonstrated great outcomes in solving nonlinear regression estimation problems and to utilize the linear strength of ARIMA. Comparison with other models using different datasets has been done and the results are very promising.

Keywords

Time series Forecasting Hybrid model SVM ARIMA Combination 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringBVM Engineering CollegeVallabh VidyanagarIndia
  2. 2.Department of Information TechnologyCSPIT, CHARUSATAnandIndia
  3. 3.Department of Information TechnologyA.D. Patel Institute of TechnologyAnandIndia

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