Conv1D + LSTM Call Volume Forecasting

📌 This project was developed end-to-end by Xichun Han as a demonstration of time series forecasting with hybrid deep learning.
It is shared under the MIT License for educational purposes. If reused, proper attribution is expected.

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Overview

This project builds a deep learning pipeline to forecast daily inbound call volumes using a hybrid Conv1D + LSTM architecture. The code is structured for reproducibility using fully simulated data and mimics real-world capacity trends, holiday effects, and seasonal fluctuations.

Originally built to support staffing optimization at luxury travel company, this forecasting system reduced error by ~30% compared to Excel-based manual trendlines.

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Highlights

• Model: Conv1D layers for pattern extraction + LSTM layers for sequential memory
• Features: Simulated business metrics (RollCap, monthly_sum), U.S. holidays, and calendar signals
• Optimization: Hyperparameter tuning with GridSearchCV + TimeSeriesSplit
• Stack: TensorFlow/Keras, scikit-learn, pandas, holidays
• Performance: MAE ~0.0485 (scaled), significant uplift vs. baseline

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Repository Structure

├── generate_dummy_data.py
├── generate_monthly_sum_data.py
├── generate_rollcap_dummy_data.py
├── LSTM_CONV1D_Model.py
├── LSTM_CONV1D_Forecast.ipynb
├── requirements.txt
└── README.md

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Features

Feature	Description
Total_Presented	Target variable – daily inbound calls
RollCap	30-day rolling sum of projected capacity
monthly_sum	Total calls in month – used as trend proxy
Holiday_A	Full-company closure (e.g., Christmas)
Holiday_B	Reduced operation holidays (e.g., Labor Day)
weekday, week_number, month	Calendar-based seasonality

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Model Architecture

• Input: (7, 8) sliding window of features
• Conv1D Layers: 64 & 32 filters (kernels 5 & 3), ReLU
• LSTM Layers: 3 stacked LSTMs with dropout & L2 regularization
• Dense Layers: 32-unit hidden layer + 1-unit output
• Optimization: Adam / Nadam, learning rate grid, batch size grid
• Loss Function: Mean Squared Error (MSE)
• EarlyStopping: Stops when training loss stagnates

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How to Run

# Step 1: Generate synthetic training + future data
python generate_dummy_data.py
python generate_monthly_sum_data.py
python generate_rollcap_dummy_data.py

# Step 2: Train and forecast
python LSTM_CONV1D_Model.py

Model outputs:

• Scaled and original-scale MAE/MSE
• Forecast plot vs historical
• Future prediction CSV (optional)

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Results

Metric	Value
Scaled MAE	~0.0485
Original MAE Estimate	~300–500 calls/day (approx.)
Visual Forecast Match	Strong trend alignment across validation and future windows

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Business Context

In real deployments, this architecture helped predict call demand across a 12-month horizon, accounting for cruise seasonality, promotional spikes, and U.S. holiday effects. It enabled more efficient scheduling, cost reduction, and faster decision-making in both sales and service departments.

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Author

Xichun (Harrison) Han
LinkedIn

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📈 Forecast Visualization

Below is a sample forecast from the Conv1D + LSTM model using simulated data.

Forecast vs. Actual using dummy data (for architecture demonstration only)

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License

MIT License. See LICENSE file for details.
Use allowed for educational purposes. Attribution is required for reuse.