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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Process of Sales Prediction using ARIMA and SARIMAX to Forecast Future Sales |
| Authors Name: | V.Rupika Priyatham , Prof.R.J.Rama Sree |
| Unique Id: | IJSDR2304339 |
| Published In: | Volume 8 Issue 4, April-2023 |
| Abstract: | Sales forecasting is an essential component of business planning and decision-making. Time series analysis is a powerful statistical method for identifying patterns in historical data and forecasting future values. The use of Autoregressive Integrated Moving Averages (ARIMA) and Seasonal Autoregressive Integrated Moving Averages with eXogenous variables) (SARIMAX) models for sales prediction is the focus of this paper. The paper describes and provides a comprehensive guide to the general methodology for developing ARIMA and SARIMAX models for sales forecasting. Data visualization, making the data stationary, plotting correlation and autocorrelation charts, building the model, and making predictions are all part of the process. This paper presents a step-by-step methodology for developing ARIMA and SARIMAX models for sales forecasting, as well as the steps involved in using a machine learning model, specifically ARIMA, to forecast sales, such as data preparation, determining the order of differencing, determining the order of the ARMA model, fitting the ARIMA model, evaluating the model, and forecasting sales. The findings of this research paper can assist businesses in making more accurate sales forecasts and improving their decision-making processes. |
| Keywords: | Sales, Prediction, ARIMA, SARIMAX, Forecasting, Time Series, Analysis, Accuracy |
| Cite Article: | "Process of Sales Prediction using ARIMA and SARIMAX to Forecast Future Sales", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.2183 - 2186, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304339.pdf |
| Downloads: | 000337072 |
| Publication Details: | Published Paper ID: IJSDR2304339 Registration ID:205681 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: 2183 - 2186 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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