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 pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9964190/ |
| _version_ | 1811206886927630336 |
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| author | Uppala Meena Sirisha Manjula C. Belavagi Girija Attigeri |
| author_facet | Uppala Meena Sirisha Manjula C. Belavagi Girija Attigeri |
| author_sort | Uppala Meena Sirisha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T03:55:31Z |
| format | Article |
| id | doaj.art-ebd97165f9734736a4c18c066b121372 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T03:55:31Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ebd97165f9734736a4c18c066b1213722022-12-22T03:48:52ZengIEEEIEEE Access2169-35362022-01-011012471512472710.1109/ACCESS.2022.32249389964190Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A ComparisonUppala Meena Sirisha0Manjula C. Belavagi1https://orcid.org/0000-0002-3243-1310Girija Attigeri2https://orcid.org/0000-0001-9815-3256Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaTime 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.https://ieeexplore.ieee.org/document/9964190/Statistical methodstime series forecastingdeep learningprofit predictionARIMASARIMA |
| spellingShingle | Uppala Meena Sirisha Manjula C. Belavagi Girija Attigeri Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison IEEE Access Statistical methods time series forecasting deep learning profit prediction ARIMA SARIMA |
| title | Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison |
| title_full | Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison |
| title_fullStr | Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison |
| title_full_unstemmed | Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison |
| title_short | Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison |
| title_sort | profit prediction using arima sarima and lstm models in time series forecasting a comparison |
| topic | Statistical methods time series forecasting deep learning profit prediction ARIMA SARIMA |
| url | https://ieeexplore.ieee.org/document/9964190/ |
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