This project focuses on analyzing and forecasting daily support incident volumes to help operations and support teams plan resources more effectively. Using time series analysis and forecasting techniques, historical incident data was explored to identify trends, seasonality, and patterns. Multiple forecasting models were built and evaluated to determine the most reliable approach for proactive planning.
- Analyze historical incident volume trends and seasonality
- Identify weekly and monthly incident patterns
- Build and evaluate time series forecasting models
- Compare model performance using error metrics
- Provide actionable insights for operational planning
- Time Series Decomposition (Trend & Seasonality)
- Rolling Mean & Standard Deviation Analysis
- Stationarity Check using Differencing
- ACF & PACF Analysis
- ARIMA, SARIMA & Holt–Winters Forecasting Models
- Train–Test Split for Time Series
- Residual Diagnostics & Model Validation
- Model Comparison using MAE
Displays the total number of incidents recorded per day.
Shows how incident volume changes over time, highlighting long-term patterns.
Smooths short-term fluctuations to clearly reveal the underlying trend.
Highlights which weekdays experience higher incident volumes.
Shows monthly seasonality patterns.
Used to check stationarity by observing mean and variance stability.
Helps stabilize the time series and remove trend components.
Illustrates how historical data is split for forecasting evaluation.
Identifies autocorrelation across different lag values.
Helps determine the appropriate AR terms for the model.
Compares predicted incident volumes against actual observed values.
Checks whether residuals are randomly distributed over time.
Validates the normality assumption of model residuals.
Ensures no significant autocorrelation remains after modeling.
Compares forecasting models based on performance metrics such as MAE.
Displays the predicted daily incident volumes for upcoming days based on the final SARIMA model, helping support proactive staffing and capacity planning.
- Incident volumes show clear weekly and monthly seasonality
- SARIMA outperformed ARIMA based on MAE
- First differencing was required to achieve stationarity
- Residual diagnostics confirmed model reliability
- Forecasts can support staffing and workload planning
- Python
- Pandas & NumPy
- Matplotlib & Seaborn
- Statsmodels
- Scikit-learn
- Jupyter Notebook
1. Clone the Repository:
git clone https://github.com/indu-explores-data/Support-Incident-Volume-Forecasting.git
2. Navigate to the Project Directory:
cd Support-Incident-Volume-Forecasting
3. Create and Activate a Virtual Environment:
python -m venv venv
Windows:
venv\Scripts\activate
Mac/Linux:
source venv/bin/activate
4. Install Required Libraries:
pip install -r requirements.txt
5. Launch Jupyter Notebook:
jupyter notebook
6. Open Support-Incident-Volume-Forecasting.ipynb and run all cells to reproduce the analysis.
- Open Support-Incident-Volume-Forecasting.ipynb in Jupyter Notebook
- Run all cells sequentially
- Explore visualizations and model comparisons
- Final forecasts available in model output cells
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