Research Article
Analysis of Time Series Prediction using Recurrent Neural Networks
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 182 - Issue 48 |
| Published: Apr 2019 |
| Authors: Gaurav Yadav, Richa Vasuja |
10.5120/ijca2019918732
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Gaurav Yadav, Richa Vasuja . Analysis of Time Series Prediction using Recurrent Neural Networks. International Journal of Computer Applications. 182, 48 (Apr 2019), 34-40. DOI=10.5120/ijca2019918732
@article{ 10.5120/ijca2019918732,
author = { Gaurav Yadav,Richa Vasuja },
title = { Analysis of Time Series Prediction using Recurrent Neural Networks },
journal = { International Journal of Computer Applications },
year = { 2019 },
volume = { 182 },
number = { 48 },
pages = { 34-40 },
doi = { 10.5120/ijca2019918732 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2019
%A Gaurav Yadav
%A Richa Vasuja
%T Analysis of Time Series Prediction using Recurrent Neural Networks%T
%J International Journal of Computer Applications
%V 182
%N 48
%P 34-40
%R 10.5120/ijca2019918732
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Time series prediction is the heart of forecasting data that is based on past information of any particular dataset, recurrent neural network combines with the time series algorithm and provide much reliable outcomes having high matching efficiency with actual real-time results as the combination of RNN and time series can predict almost anything which has time as its managing factor as RNN has ability to iterate efficiency with time feeds, it tells about the trend of future and it is particularly important in prediction of crucial data such as weather forecast or financial data, because proper forecast can provide the vital help and safety or the advancement for the change, though the future is uncertain but people must know their future as near it could be to the future. Based on the research this paper contains analytical data of recurrent neural network and its use with time series alongside the experimental data analysis of weather forecast and financial forecast data.
References
- Georg Dorffner, Neural Networks for Time series prediction, 1996
- Jeffrey Elman. “Finding Structure in Time”. In: Cognitive Science 14 (Mar. 1990). DOI: 10.1016/0364-0213(90)90002-E.
- K. Greff et al. “LSTM: A Search Space Odyssey”. In: IEEE Transactions on Neural Networks and Learning Systems 28.10 (Oct. 2017), pp. 2222–2232. ISSN: 2162-237X
- François Chollet, “Keras”, 2015, In: Github Journal
- TensorFlow, 2015, “TensorFlow: Large-scale machine learning on heterogeneous systems”, https://www.tensorflow.org/about/bib
- Google Trends. URL: https://trends.google.com (visited on 10/01/2018).
- Yahoo Finance - Business Finance, Stock Market, Quotes, News. URL: https : / / finance . yahoo.com/ (visited on 10/01/2018).
- Bitcoincharts. URL: https://bitcoincharts.com/ (visited on 10/01/2018).
- A. Meyler, G. Kenny, and T. Quinn, “Forecasting Irish Inflation Using ARIMA Models,” Technical Paper 3/RT/1998, Central Bank of Ireland Research Department, 1998.
- http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/ (visited on 19/03/2019)
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