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Forecasting / Retail Analytics

Demand Forecasting with ARIMA and Prophet

A comparative forecasting system that applies multiple time-series methods to retail sales data, delivering a 15% improvement in forecast accuracy over baseline approaches and providing clear demand projections for inventory planning.

Type
Time Series Analysis
Domain
Retail / Demand Planning
Improvement
15% over baseline
Status
Completed

The Challenge

Retailers face volatile demand fluctuations driven by seasonality, promotions, external events, and shifting consumer behaviour. Baseline forecasting methods often fail to capture these temporal patterns, leading to either overstocking (tying up capital) or understocking (losing sales).

Accurate demand forecasting directly impacts profitability. Even small improvements in forecast accuracy translate into significant savings in inventory costs and reduced lost revenue from stockouts.

Approach

01
Time Series Decomposition
Decomposed the historical sales data into trend, seasonal, and residual components to understand the underlying patterns driving demand fluctuations.
02
Feature Engineering
Created temporal features including lag variables, rolling statistics, holiday indicators, and seasonal encodings to enrich the forecasting models.
03
Model Development
Built and compared ARIMA/SARIMA models for statistical rigour and Prophet for handling multiple seasonality patterns and holiday effects.
04
Evaluation and Selection
Evaluated models using RMSE, MAE, and MAPE against baseline methods, selecting the optimal approach for each product category.
TIME SERIES FORECASTING
+15%

Results

15%
Accuracy improvement over baseline
Multi-Model
ARIMA, SARIMA, and Prophet compared
Production
Ready for integration with inventory systems

The Prophet-based approach performed best on products with strong seasonal patterns and holiday effects, while ARIMA excelled on more stationary series. The hybrid approach of selecting the best model per category delivered the overall 15% improvement, providing demand planners with reliable projections for inventory optimisation.

Technology Stack

Python Statsmodels Prophet ARIMA SARIMA Pandas Matplotlib
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