Sales Trend Analysis and Forecasting Using Time Series Data Analytics Models

The objective of this project is to analyze historical sales data to identify trends, seasonal patterns, and forecast future sales. This project helps organizations improve demand planning and inventory management through data-driven forecasting techniques.

Gather historical sales datasets from retail or business domains.

Preprocess data by handling missing records and formatting time-based variables.

Conduct exploratory data analysis to observe sales distributions and growth patterns.

Identify seasonality, trend, and cyclic behavior in sales data.

Apply time series models such as moving averages or ARIMA.

Train and validate forecasting models using historical data.

Evaluate model performance using error metrics like MAE and RMSE.

Visualize sales trends and forecast results using line charts.

Interpret forecasting outputs and business implications.

Document assumptions, limitations, and results.

Present recommendations for business planning and decision-making.