De-seasoning-Based Time Series Data Forecasting Method Using Recurrent Neural Network (RNN) and Tensor Flow

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 526)


Time series data forecasting is studied by various method until today and has many applications in various fields like stock prediction, contextual chat bots, cognitive search engines, etc. Also till date, many stats models have been developed like ARIMA, ARMA, etc. A new approach is designed for time series data forecasting using RNN with Tensorflow framework, developed by Google for various types of neural networks modelling. De-seasoning of data is also carried out to study and obtains better results in this paper, a comparison chart for the same is presented, helps in aligning the contextual information on chat bot programs, and also is better for other data analysis like context search. This approach also helps us in reducing the training losses to increase in the accuracy of forecasting.


Recurrent neural networks De-seasoned data Time series forecasting Tensor flow 



We would like to thank Mr. Vimal Kumar for helping us out in developing the code for neural network in python and supervising the server while the training was in progress.


  1. 1.
  2. 2.
    Wikibooks. Artificial Neural Networks/Error-CorrectionLearning (2013). -Correction_Learning
  3. 3.
    Allende, H., Moraga, C., Salas R.: Artificial neural networks in time series forecasting: a comparative analysisGoogle Scholar
  4. 4.
    Sundermeyer, M., Ney, H., Schlüter, R.: From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Trans. Audio Speech Lang. Process. 23(3), 517–529 (2015)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Fukushima, K.: Neural network model for a mechanism of pattern recognition unaffected by shift in position - Neocogni- tron. Trans. IECE. J62-A(10), 658–665 (2012)Google Scholar
  7. 7.
    Sodanil, M., Chatthong, P.: Artificial neural network-based time series analysis forecasting for the amount of solid waste in Bangkok. In: 2014 Ninth International Conference on Digital Information Management (ICDIM) (2014)Google Scholar
  8. 8.
    Shamshiry, E., et al.: Forecasting generation waste using artificial neural networks. In: Proceedings of the 2011 International Conference on Artificial Intelligence (ICAI 2011), Las Vegas, NV, USA, 18–21 July 2011Google Scholar
  9. 9.
    Subrahmanian, V.S., et al.: The Twitter bot challenge (2016).
  10. 10.
    Goodfellow, I.J., et al.: Generative adversarial networks. 10 June 2014
  11. 11.
  12. 12.
  13. 13.
  14. 14.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Jaypee Institute of Information TechnologyNoidaIndia

Personalised recommendations