Forecasting Sales Data Using Time Series Models and LSTM Model

In the framework of sales forecasting, the project investigates the field of time series forecasting, with a particular emphasis on analyzing and comparing the forecasting precision of ARIMA, SARIMA, and LSTM models. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are some of the performance metrics that are utilized in this procedure. It's a thorough workflow that includes data preparation, rigorous model training, and diligent assessment. To facilitate a comparative comparison of the forecasting capabilities of the models, the visualization component uses a variety of graphical representations, such as incisive line graphs and instructive bar charts. Determining which model is the most capable of providing accurate sales forecasts is the key objective. This has significant implications for improving decision-making processes in the areas of sales and inventory management. This research program helps companies to design flexible and proactive strategies that are customized to changing market landscapes. As a result, enterprises can enhance their performance and strategic resilience. This is accomplished by providing businesses with data-driven insights and rigorous forecasting tools.