time_series-analysis-customised-dashboard/help_in_running.txt at main · rk-python5/time_series-analysis-customised-dashboard · GitHub

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# 🚀 Time Series Forecasting Project - Manual Setup Guide

## 📋 Prerequisites
- Python 3.8 or higher installed
- Terminal/Command Prompt access
- Internet connection (for dependencies and stock data)

---

## 🔧 Step-by-Step Setup Instructions

### Step 1: Navigate to Project Directory
```bash
cd /Users/rehaankhatri/ATSA_project_sem_7/Time_Series_Analysis_Project
```

### Step 2: Activate Virtual Environment (Optional but Recommended)
```bash
# Create virtual environment if it doesn't exist
python3 -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
# venv\Scripts\activate
```

### Step 3: Install Dependencies
```bash
pip install flask flask-cors pandas numpy matplotlib seaborn plotly scikit-learn statsmodels yfinance requests python-dotenv
```

### Step 4: Run the Flask Application
```bash
python app_flask.py
```

### Step 5: Access the Application
Open your web browser and go to:
- **Local:** `http://127.0.0.1:5001` or `http://localhost:5001`
- **Network:** `http://192.168.29.98:5001` (accessible from other devices)

---

## 🎯 Quick Start Guide

### 1. Load Data
- Click **"Load Sample Data"** for quick testing
- Or enter a stock ticker (like AAPL) and click **"Load Data"**

### 2. Run Forecasting
- Select model: **ARIMA** or **SARIMA**
- Adjust forecast periods (default: 30)
- Set confidence interval (default: 95%)
- Click **"Run Forecasting"**

### 3. View Results
- Performance metrics (RMSE, MAE, MAPE, AIC)
- Interactive forecast charts
- Residuals analysis

---

## 🔧 Troubleshooting

### If you get dependency errors:
```bash
pip install --upgrade pip
pip install -r requirements.txt
```

### If port 5001 is busy:
```bash
# Kill any process using port 5001
lsof -ti:5001 | xargs kill -9
# Then run the app again
python app_flask.py
```

### If you get PyArrow/Prophet errors:
Don't worry! The project now works without Prophet. Just use ARIMA and SARIMA models.

### If Flask doesn't start:
```bash
# Check if all files are present
ls -la app_flask.py models/ utils/ static/ templates/

# Make sure you're in the right directory
pwd
```

---

## 📱 Alternative: Run Streamlit Version (If Available)
```bash
# If you want to try Streamlit (may have dependency issues)
pip install streamlit
streamlit run app.py
```

---

## 🛑 To Stop the Application
- Press `Ctrl + C` in the terminal
- Or close the terminal window

---

## 📋 Complete Command Sequence (Copy & Paste)
```bash
# Navigate to project
cd /Users/rehaankhatri/ATSA_project_sem_7/Time_Series_Analysis_Project

# Activate virtual environment (optional)
source venv/bin/activate

# Install dependencies
pip install flask flask-cors pandas numpy matplotlib seaborn plotly scikit-learn statsmodels yfinance requests python-dotenv

# Run the application
python app_flask.py

# Open browser to http://localhost:5001
```

---

## ✅ Success Indicators
- Terminal shows: `* Running on http://127.0.0.1:5001`
- Browser loads the dashboard successfully
- You can load sample data and run forecasts
- Charts display properly

---

## 🎉 Features Available
- **Data Sources:** Stock data (Yahoo Finance), CSV upload, Sample data
- **Models:** ARIMA, SARIMA (Prophet disabled due to dependency issues)
- **Visualizations:** Interactive charts with Plotly
- **Performance Metrics:** RMSE, MAE, MAPE, AIC, BIC
- **Fast Mode:** Enabled by default for better performance
- **Error Handling:** Comprehensive validation and fallbacks

---

## 📞 Support
If you encounter any issues:
1. Check the terminal output for error messages
2. Ensure all dependencies are installed
3. Verify you're in the correct directory
4. Try the troubleshooting steps above

---

## 🔗 Repository Information
- **GitHub:** https://github.com/aditya-cadbury/Time_Series_Analysis_Project.git
- **Last Updated:** September 2025
- **Project Type:** ATSA Major Project for Forecasting

---

**Your Time Series Forecasting Dashboard should now be running perfectly!** 🎉