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

Uppala Meena Sirisha, Manjula C. Belavagi, Girija Attigeri

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)124715-124727
Number of pages13
JournalIEEE Access
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


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