Financial-Forecasting-Decision-Support

A comprehensive financial forecasting tool using ARIMA models, scenario analysis, and decision support features. Implements time series analysis with validation metrics (MAPE, RMSE) and budget planning capabilities.

Features

• ARIMA Time Series Forecasting: Automatic parameter selection and model fitting
• Scenario Analysis: Generate best-case, base-case, and worst-case forecasts
• Performance Metrics: MAPE, RMSE, MAE, MASE calculations
• Budget Planning: Integrated budget allocation and variance analysis
• PDF Report Generation: Professional 5-page PDF reports with visualizations
• Decision Support: Actionable insights and recommendations

Quick Start

Installation

git clone https://github.com/Kishan-Sinha/Financial-Forecasting-Decision-Support.git
cd Financial-Forecasting-Decision-Support
pip install -r requirements.txt

Generate Sample Reports

python generate_sample_reports.py

This will create Financial_Forecasting_Report.pdf in the current directory.

Run Full Analysis

python main.py

For custom data, modify the data source in data_loader.py.

Project Structure

.
├── data_loader.py              # Data loading and preprocessing
├── forecast_model.py          # ARIMA model implementation
├── scenario_analysis.py       # Scenario generation module
├── visualization_report.py    # Chart and visualization generation
├── report_engine.py          # PDF report generation engine
├── generate_sample_reports.py # Demo script
├── main.py                   # Main execution script
├── requirements.txt           # Dependencies
├─┠─ sample_outputs/            # Sample PDF reports location
├┤── DOCUMENTATION.md          # Comprehensive usage guide
├── REPORT_GENERATION.md       # PDF generation details
└── SAMPLE_REPORT_OUTPUT.md    # Sample report documentation

Generated PDF Reports

Sample Report Structure

The system generates professional 5-page PDF reports containing:

1. Title & Executive Summary

   • Project overview and key findings
   • Dataset statistics
   • Analysis period

2. ARIMA Forecast Visualization

   • Historical data plot (blue line)
   • 12-month forecast (red line)
   • 95% confidence intervals (shaded area)

3. Scenario Analysis

   • Best-case scenario (+10% growth)
   • Base-case forecast (ARIMA model)
   • Worst-case scenario (-10% growth)
   • Comparative table

4. Performance Metrics

   • MAPE (Mean Absolute Percentage Error)
   • RMSE (Root Mean Squared Error)
   • MAE (Mean Absolute Error)
   • MASE (Mean Absolute Scaled Error)

5. Budget Planning & Recommendations

   • Budget allocation charts
   • Variance analysis
   • Decision recommendations

Usage Examples

Basic Forecast

from forecast_model import ARIMAForecaster
from data_loader import load_financial_data

data = load_financial_data()
forecaster = ARIMAForecaster(data)
forecast = forecaster.fit_predict(periods=12)
metrics = forecaster.calculate_metrics()
print(f"MAPE: {metrics['mape']:.2f}%")

Generate Scenarios

from scenario_analysis import ScenarioAnalyzer

analyzer = ScenarioAnalyzer(forecast_data)
best_case = analyzer.best_case_scenario(growth_rate=0.10)
base_case = analyzer.base_case_scenario()
worst_case = analyzer.worst_case_scenario(growth_rate=-0.10)

Create PDF Report

from report_engine import ReportGenerator

report_gen = ReportGenerator(forecast, metrics, scenarios)
report_gen.generate_pdf('Financial_Forecasting_Report.pdf')

PDF Report Files Location

Generated PDF reports are saved in the sample_outputs/ directory:

sample_outputs/
├── README.md                          # Report documentation
├── Financial_Forecasting_Report.pdf   # Main report (generated locally)
└── [additional reports here]          # More custom reports

Note: PDF files are generated when you run python generate_sample_reports.py locally. See sample_outputs/README.md for detailed instructions.

Key Modules

data_loader.py

Loads and preprocesses financial time series data from various sources.

forecast_model.py

Implements ARIMA time series forecasting with automatic parameter optimization.

scenario_analysis.py

Generates multiple forecast scenarios for decision support.

visualization_report.py

Creates professional charts and visualizations for PDF reports.

report_engine.py

Core PDF generation engine using ReportLab.

Requirements

• Python 3.7+
• pandas
• numpy
• statsmodels (ARIMA)
• scikit-learn
• matplotlib
• reportlab (PDF generation)

Dataset

The system uses financial time series data (120 months) with:

• Monthly frequency
• Multiple years of historical data
• Suitable for ARIMA modeling

Performance Metrics Explained

• MAPE: Mean Absolute Percentage Error (0-100%, lower is better)
• RMSE: Root Mean Squared Error (in original data units)
• MAE: Mean Absolute Error (average deviation)
• MASE: Mean Absolute Scaled Error (comparison to naive forecast)

Customization

1. Change Data Source: Update data_loader.py
2. Adjust ARIMA Parameters: Modify forecast_model.py
3. Custom Scenarios: Edit scenario_analysis.py
4. Report Styling: Update report_engine.py

Documentation

• DOCUMENTATION.md - Comprehensive usage guide
• REPORT_GENERATION.md - Detailed PDF generation guide
• SAMPLE_REPORT_OUTPUT.md - Sample report structure and examples
• sample_outputs/README.md - Report files and execution instructions

Workflow

Raw Data
   → Data Loading & Preprocessing
   → ARIMA Model Fitting
   → Forecast Generation (12 months)
   → Scenario Analysis (Best/Base/Worst)
   → Performance Metrics Calculation
   → Visualization Creation
   → PDF Report Generation

Author

Kishan-Sinha

License

MIT License