Sales Forecasting Dashboard using Time Series Analysis

Forecasts future sales from historical data using time-series models.

Businesses that order stock, hire staff or plan cash flow all need a reliable view of next month's sales, yet many still guess from last year's numbers. Sales data has patterns such as trend, weekly and yearly seasonality and holiday spikes that a good model can capture. This project forecasts future sales from historical data and presents the forecast in an interactive dashboard.

The project starts with data cleaning and exploratory analysis, showing trend and seasonality by decomposing the series. It then builds forecasts with classical ARIMA models, which require the series to be made stationary and their parameters chosen carefully, and with Prophet, which handles seasonality, holidays and missing data with less tuning. Models are validated with a rolling-origin backtest and compared with error measures such as MAE, RMSE and MAPE, and a simple seasonal baseline is included so that any gain is real. A Plotly dashboard lets a user pick a product or region, choose the forecast horizon and see the forecast with confidence intervals, plus a breakdown of trend and seasonal components. The write-up explains how to interpret uncertainty and when forecasts should not be trusted.

You will learn time-series analysis, model validation and interactive visualization. The Project Reference Guide explains the methods, and the Reference Implementation contains the notebooks and dashboard.