Sales forecasting plays a crucial role in the retail industry, helping businesses predict future sales, optimize inventory management, and improve decision-making. Retailers face challenges in accurately predicting demand due to various factors such as seasonality, promotions, and external economic conditions. Machine learning techniques can help provide more accurate forecasts by learning from historical sales data and identifying patterns.
This workflow will demonstrate the use of machine learning (i.e ARIMA, SARIMA), specifically time series forecasting methods, to predict sales. This lab activity includes:
Preprocess historical sales data.
Data Analysis on time series data
Train a forecasting model.
Evaluate the model's performance.
Generate future sales predictions.