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Document details - Time series forecasting using ARIMA and Recurrent Neural Net with LSTM network

Journal Volume 7, Issue 2, March - April 2018, Article 8671972 Avinash Nath, Abhay Katiyar, Srajan Sahu, Prof. Sanjeev Kumar , " Time series forecasting using ARIMA and Recurrent Neural Net with LSTM network" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 7, Issue 2, March - April 2018 , pp. 065-069 , ISSN 2278 - 6856.

Time series forecasting using ARIMA and Recurrent Neural Net with LSTM network

    Avinash Nath, Abhay Katiyar, Srajan Sahu, Prof. Sanjeev Kumar

Abstract

Abstract: Autoregressive integrated moving average (ARIMA) or Box-Jenkins Method is a popular linear model for time series forecasting over the decade. Recent research has shown that the use of artificial neural net improves the accuracy of forecasting to large extent. We are proposing the solution in order to extract both Linear and Non-Linear components in data. In this paper, we propose a solution to predict highly accurate results using an aggregation of ARIMA and ANN (Recurrent neural net) to extract Linear and Non-Linear Component of data respectively. Keywords: ARIMA, Box–Jenkins methodology, artificial neural networks, Time series forecasting, recurrent neural net, combined forecasting, long short-term memory.

  • ISSN: 22786856
  • Source Type: Journal
  • Original language: English

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