Analysis of Financial Time Series Forecasting using Deep Learning Model

Raghavendra Kumar · Pardeep Kumar · Yugal Kumar

39 citations0/0 files storedInternational Conference on Cloud Computing, Data Science & Engineering (Confluence)

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

Time series data analysis and its forecasting is a foremost trend of stock market prediction. Accurate prediction of stocks brings more profit to market traders and helps in financial decision making. There are various machine learning and deep learning models assist to predict the stock market accuracy. Recent work concludes that various models like Support Auto Regressive Integrated Moving Average (ARIMA), Vector Machine (SVM), Artificial Neural Network (ANN), XGBoost, and Recurrent Neural Network (RNN) were preferred to obtain improved accuracy. In this study, a stacked Long Short-Term Memory (LSTM) model is proposed to predict the stock market accuracy and proposed model is compared with Moving Average (MA) and XGBoost models. The experiments are performed on the historical dataset of Infosys Limited of Bombay Stock Exchange, India (BSE30). The model is also evaluated through performance measures Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) and found that proposed stacked LSTM model outperformed the benchmark model.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)2014Stock Price Prediction Using the ARIMA Model 2017Stock market's price movement prediction with LSTM neural networks 2018NSE Stock Market Prediction Using Deep-Learning Models 2014Predicting stock market index using fusion of machine learning techniques 2019Stock Market Analysis: A Review and Taxonomy of Prediction Techniques 2018Predicting the direction of stock market prices using tree-based classifiers

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