TY - GEN
T1 - Comparative analysis of time-series forecasting algorithms for stock price prediction
AU - Joosery, Baleshwarsingh
AU - Deepa, G.
PY - 2019/11/15
Y1 - 2019/11/15
N2 - This paper predicts the average stock price for five datasets by utilizing the historical stock price data ranging from April 2009 to February 2019. Autoregressive Integrated Moving Average (ARIMA) model is used to generate the baseline, while Long Short-Term Memory (LSTM) networks is used to build the forecasting model for predicting the stock price. The efficiency of the two models is compared in terms of Mean Squared Error. The results show that the LSTM model predicts better than the ARIMA model with respect to time series forecasting. Additionally, Attention LSTM networks is employed to further study the improvement in accuracy of the stock price forecasting model.
AB - This paper predicts the average stock price for five datasets by utilizing the historical stock price data ranging from April 2009 to February 2019. Autoregressive Integrated Moving Average (ARIMA) model is used to generate the baseline, while Long Short-Term Memory (LSTM) networks is used to build the forecasting model for predicting the stock price. The efficiency of the two models is compared in terms of Mean Squared Error. The results show that the LSTM model predicts better than the ARIMA model with respect to time series forecasting. Additionally, Attention LSTM networks is employed to further study the improvement in accuracy of the stock price forecasting model.
UR - http://www.scopus.com/inward/record.url?scp=85078454394&partnerID=8YFLogxK
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U2 - 10.1145/3373477.3373699
DO - 10.1145/3373477.3373699
M3 - Conference contribution
AN - SCOPUS:85078454394
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the International Conference on Advanced Information Science and System, AISS 2019
PB - Association for Computing Machinery (ACM)
T2 - 2019 International Conference on Advanced Information Science and System, AISS 2019
Y2 - 15 November 2019 through 17 November 2019
ER -