Application of Artificial neural networks in Time series forecasting
time series forecasting, artificial neural networks, ARIMA model, LSTM
The most important lagged components in time series forecasting can be found using an advanced method introduced in this article called an artificial neural network model is Long Short-Term Memory (LSTM). Additionally, this article compares the forecasting accuracy of the traditional ARIMA model utilizing time series data with the artificial neural network model is LSTM. Collected rainfall data for India from 1901 to 2015. According to our findings, the coefficient of multiple determination (R2), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), the best predicting accuracy is offered by Long Short-Term Memory (LSTM) neural networks, which are more advanced than traditional time series approaches and the traditional ARIMA model.
"Application of Artificial neural networks in Time series forecasting", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.154 - 158, November-2022, Available :https://ijsdr.org/papers/IJSDR2211025.pdf
Volume 7
Issue 11,
November-2022
Pages : 154 - 158
Paper Reg. ID: IJSDR_202466
Published Paper Id: IJSDR2211025
Downloads: 000347218
Research Area: Engineering
Country: -, -, -
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
