Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison

Time series forecasting using historical data is significantly important nowadays. Many fields such as finance, industries, healthcare, and meteorology use it. Profit analysis using financial data is crucial for any online or offline businesses and companies. It helps understand the sales and the profits and losses made and predict values for the future. For this effective analysis, the statistical methods- Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA models (SARIMA), and deep learning method- Long Short- Term Memory (LSTM) Neural Network model in time series forecasting have been chosen. It has been converted into a stationary dataset for ARIMA, not for SARIMA and LSTM. The fitted models have been built and used to predict profit on test data. After obtaining good accuracies of 93.84% (ARIMA), 94.378% (SARIMA) and 97.01% (LSTM) approximately, forecasts for the next 5 years have been done. Results show that LSTM surpasses both the statistical models in constructing the best model.

Automated summary

Time series forecasting using historical data is crucial in various fields. In this study, ARIMA, SARIMA, and LSTM models were used to analyze profits and make predictions. The results showed that the LSTM model outperformed the statistical models in terms of accuracy.