Time-Series-Forecasting

This repository contains all necessary training methods, datasets, and works related to the development of Intelligent Demand Forecasting for Supply Chain Management.

Running the Backend Service

Setup Environment

Before running the service, you need to set up a Python virtual environment:

Create and Activate Virtual Environment

On Windows:

# Create virtual environment
python -m venv .venv

# Activate virtual environment
.venv\Scripts\activate

On macOS/Linux:

# Create virtual environment
python3 -m venv .venv

# Activate virtual environment (or Git Bash)
source .venv/Scripts/activate

Install Dependencies

# Install requirements
cd forecasting_service
pip install -r requirements.txt

Django Commands

The project includes a Makefile with the following commands to manage the Django backend service:

• Ensure that you are in the project's root directory where the Makefile is located.

Starting the Server

# Start the Django server with migrations
make run-server

This will:

1. Create migrations for the forecastApp
2. Apply migrations to the database
3. Start the Django server at http://localhost:8000/

Other Commands

Access Django Shell

make shell

Run Tests

make test

Clear and Recreate Migrations

make clear-migrations

This will delete all migration files (except __init__.py), then recreate and apply migrations.

Start Server with Fake Migrations

make run-server-fake

Use this when you need to run the server with --fake-initial flag for migrations.