Machine Learning for Time Series Data Prediction

This ongoing project focuses on developing advanced predictive analytics for the client’s time series data, specifically sales revenue and inventory levels. Currently, the client relies on their buying team and inventory coordinators to manually report data and provide estimates for future periods. The client is seeking a more scientific, data-driven approach that leverages statistical methodologies to improve forecasting accuracy, with the goal of enhancing business planning with higher confidence and reliability for future periods.

The client’s data warehouse provides access to clean datasets, enabling me to conduct predictive analysis by directly draw data from it. I explored several machine learning models, including ARIMA, LSTM, Prophet, and XGBoost, to determine the most suitable approach for forecasting sales and inventory data. After thorough evaluation and comparison of model performance, I determined that Prophet offers the best results for this specific use case, providing relatively accurate predictions for future business planning.

Comparing prediction result with actual data

Visualize the prediction result

The project is still in the development stage, where I am focused on fine-tuning the algorithm parameters and incorporating meaningful features to improve both the accuracy and stability of the predictions. This iterative process is aimed at refining the model’s performance to ensure it can deliver reliable and actionable forecasts for the client’s business planning needs.