{"repo":"AmirhosseinHonardoust/Demand-Forecasting","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Demand-Forecasting","clone":"git clone https://github.com/AmirhosseinHonardoust/Demand-Forecasting.git","description":"End-to-end demand forecasting with Python using synthetic time-series sales data. Includes data generation, cleaning, ARIMA/SARIMA model selection by AIC, evaluation with RMSE and MAPE, and 90-day forecasts with confidence intervals. Reproducible scripts and visualizations for portfolio showcase.","language":"Python","stars":31,"topics":["arima","data-science","data-visualization","demand-forecasting","forecasting","machine-learning","portfolio-project","predictive-modeling","python","sales-data"],"license":"MIT","category":"machine-learning","readme_excerpt":"Demand Forecasting (Time-Series) An end-to-end demand-forecasting workflow that turns a daily sales series into calibrated ARIMA/SARIMA forecasts , confidence intervals , baseline comparisons , and a rolling-origin backtest , packaged as an installable CLI with tests and CI. Important: This project is a portfolio and research demo , not a production forecasting or planning system. The data is synthetic, the model search is intentionally small, and the results are illustrative. They should not be used for real inventory, procurement, or financial planning without domain review, real data, and validation. --- Table of Contents - Project Overview - What This Project Does - What This Project Does Not Do - Key Features - System Workflow - Project Structure - Installation - Quick Start - Forecasting and Evaluation - Backtesting and Baselines - Evaluation Metrics - Visual Reports - Testing and CI - Code Quality - Limitations - Future Improvements - Tech Stack - Author - License --- Project Overview Forecasting is not only about fitting a single model. A useful forecast has to be reproducible, come with a sense of uncertainty, and be measurably better than a naive rule of thumb. This project demonstrates an end-to-end time-series workflow on a synthetic daily-sales series: - generate reproducible data with trend and seasonality - clean and gap-fill the series onto a daily index - select an ARIMA/SARIMA model by AIC over a small grid - evaluate on a hold-out validation window - produc","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Demand-Forecasting/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}