@Article{crph1341,
AUTHOR = {Bhumireddy, Jayakeshav Reddy and Chalasani, Rajiv and Tyagadurgam, Mukund Sai Vikram and Gangineni, Venkataswamy Naidu and Pabbineedi, Sriram and Penmetsa, Mitra},
TITLE = {Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models},
JOURNAL = {Current Research in Public Health},
VOLUME = {2},
YEAR = {2022},
NUMBER = {1},
PAGES = {153-164},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1341},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2022.1341},
ABSTRACT = {Financial markets have become more and more complex, so has been the number of data sources. Stock price prediction has hence become a tough but important task. The time dependencies in stock price movements tend to escape from traditional models. In this work, a hybrid ARIMA-LSTM model is suggested to enhance accuracy of stock price forecasts. Based on time series indicators like adjusted closing prices of S&P 500 stocks over a decade (2010–2019), the ARIMA-LSTM model combines influences of both autoregressive time series forecasting with the substantial sequence learning property of LSTM. Data preprocessing in all aspects including missing values interpolation, outlier’s detection and data scaling – Min-Max guarantees data quality. The model is trained on 90/10 training/testing split and met with main performance metrics: MaE, MSE & RMSE. As indicated in the results, the proposed ARIMA-LSTM model gives a MAE value and MSE value of 0.248 and 0.101 respectively and RMSE of 0.319, a measure high accuracy on stock price prediction. Coupled comparative analysis with other Artificial Neural Networks (ANN) and BP Neural Networks (BPNN) are examples of machine learning reference models, further illustrates the suitability and superiority of ARIMA-LSTM approach as compared to the underlying models with the least MAE and strong predictive capability. This work demonstrates the efficiency of integrating the classical time series models with deep learning methods for financial forecasting.},
}

%0 Journal Article
%A Bhumireddy, Jayakeshav Reddy
%A Chalasani, Rajiv
%A Tyagadurgam, Mukund Sai Vikram
%A Gangineni, Venkataswamy Naidu
%A Pabbineedi, Sriram
%A Penmetsa, Mitra
%D 2022
%J Current Research in Public Health

%@ 2831-5162
%V 2
%N 1
%P 153-164

%T Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models
%M doi:10.31586/jaibd.2022.1341
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1341

TY  - JOUR
AU  - Bhumireddy, Jayakeshav Reddy
AU  - Chalasani, Rajiv
AU  - Tyagadurgam, Mukund Sai Vikram
AU  - Gangineni, Venkataswamy Naidu
AU  - Pabbineedi, Sriram
AU  - Penmetsa, Mitra
TI  - Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models
T2  - Current Research in Public Health
PY  - 2022
VL  - 2
IS  - 1
SN  - 2831-5162
SP  - 153
EP  - 164
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1341
AB  - Financial markets have become more and more complex, so has been the number of data sources. Stock price prediction has hence become a tough but important task. The time dependencies in stock price movements tend to escape from traditional models. In this work, a hybrid ARIMA-LSTM model is suggested to enhance accuracy of stock price forecasts. Based on time series indicators like adjusted closing prices of S&P 500 stocks over a decade (2010–2019), the ARIMA-LSTM model combines influences of both autoregressive time series forecasting with the substantial sequence learning property of LSTM. Data preprocessing in all aspects including missing values interpolation, outlier’s detection and data scaling – Min-Max guarantees data quality. The model is trained on 90/10 training/testing split and met with main performance metrics: MaE, MSE & RMSE. As indicated in the results, the proposed ARIMA-LSTM model gives a MAE value and MSE value of 0.248 and 0.101 respectively and RMSE of 0.319, a measure high accuracy on stock price prediction. Coupled comparative analysis with other Artificial Neural Networks (ANN) and BP Neural Networks (BPNN) are examples of machine learning reference models, further illustrates the suitability and superiority of ARIMA-LSTM approach as compared to the underlying models with the least MAE and strong predictive capability. This work demonstrates the efficiency of integrating the classical time series models with deep learning methods for financial forecasting.
DO  - Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models
TI  - 10.31586/jaibd.2022.1341
ER  -