UR2CUTE

Using Repetitively 2 CNNs for Unsteady Timeseries Estimation

UR2CUTE is a specialized forecasting model designed for intermittent time series data. By employing a dual CNN approach with TensorFlow, it effectively addresses the challenges of predicting both the occurrence and magnitude of demand in irregular time series patterns.

📋 Overview

Intermittent demand forecasting presents unique challenges due to irregular and unpredictable demand patterns, characterized by periods of zero demand followed by random non-zero demand. Traditional forecasting methods often perform poorly on such data.

UR2CUTE employs a two-step approach:

1. A CNN-based classification model predicts demand occurrence (zero vs. non-zero)
2. A CNN-based regression model estimates the magnitude of demand

This dual-phase approach significantly improves forecasting accuracy for intermittent demand, particularly in predicting periods of zero demand.

🔍 Features

• TensorFlow Implementation: Uses TensorFlow's efficient tensor operations and GPU acceleration
• Two-Step Prediction Process: Separate models for order occurrence and quantity prediction
• Temporal Pattern Recognition: CNNs effectively capture temporal patterns in intermittent data
• Lag Feature Generation: Automatically creates lagged features to capture historical dependencies
• Combined Loss Function: Custom loss functions optimized for each prediction task
• Sklearn Compatibility: Follows scikit-learn API conventions for easy integration
• Direct Multi-Step Forecasting: Predicts multiple future time steps in one pass

📦 Dependencies

• Python 3.7+
• TensorFlow 2.x
• NumPy
• pandas
• scikit-learn

🛠️ Installation

pip install fh_prevail

Then import the model:

from fh_prevail.models import UR2CUTE

📊 Quick Start

import pandas as pd
from fh_prevail.models import UR2CUTE

# Load time series data
df = pd.DataFrame({
    'date': pd.date_range('2023-01-01', periods=50, freq='W'),
    'target': [0, 5, 0, 0, 12, 0, 0, 0, 7, 0, ...],  # Intermittent data
    'feat1': [...],  # Optional external features
    'feat2': [...]   # Optional external features
})

# Initialize model
model = UR2CUTE(
    n_steps_lag=3,
    forecast_horizon=4,
    external_features=['feat1', 'feat2']
)

# Fit model
model.fit(df, target_col='target')

# Make predictions for the next forecast_horizon steps
predictions = model.predict(df)
print("Predicted values:", predictions)

# Note: The model automatically uses GPU if available
# To manually control device placement:
# import tensorflow as tf
# device = tf.device("cuda" if tf.test.is_gpu_available() else "cpu")
# print(f"Using device: {device}")

🔧 Parameters

Parameter	Description	Default
n_steps_lag	Number of lag features to generate	3
forecast_horizon	Number of future steps to predict	8
external_features	List of column names for external features	None
epochs	Training epochs for both CNN models	100
batch_size	Batch size for training	32
threshold	Probability threshold for classifying zero vs. non-zero. Can be a float or "auto" to automatically compute the threshold from training targets	0.5
patience	Patience for EarlyStopping	10
random_seed	Random seed for reproducibility	42
classification_lr	Learning rate for classification model	0.0021
regression_lr	Learning rate for regression model	0.0021
dropout_classification	Dropout rate for classification model	0.4
dropout_regression	Dropout rate for regression model	0.2

📝 Methods

fit(df, target_col)

Fits the UR2CUTE model on the time series data.

Parameters:

• df (pandas.DataFrame): Time series data with at least the target column. Must be sorted by time.
• target_col (str): The name of the column to forecast.

Returns:

• The fitted UR2CUTE model instance.

predict(df)

Predicts the next forecast_horizon steps from the input DataFrame.

Parameters:

• df (pandas.DataFrame): Time series data with the same columns as used in fit(). Must be sorted by time.

Returns:

• numpy.ndarray: The predictions for each step in the horizon.

🔍 How It Works

1. Data Preprocessing:

   • Aggregates demand data (e.g., daily to weekly)
   • Generates lag features to capture historical patterns

2. Model Architecture:

   • Classification Model: CNN that predicts probability of non-zero demand
   • Regression Model: CNN that predicts quantity when demand exists

3. Prediction Process:

   • Classification model predicts if demand will occur
   • Regression model predicts the magnitude of demand
   • Final prediction combines both models' outputs

🏆 Performance

UR2CUTE outperforms traditional forecasting techniques including:

• Croston's method
• XGBoost
• Random Forest
• ETR
• Prophet
• AutoARIMA

Particularly for predicting intermittent demand, UR2CUTE shows significant improvements in:

• Mean Absolute Error % (MAE%)
• Root Mean Square Error % (RMSE%)
• R-squared values

📚 Citation

If you use UR2CUTE in your research, please cite:

@article{mirshahi2024intermittent,
  title={Intermittent Time Series Demand Forecasting Using Dual Convolutional Neural Networks},
  author={Mirshahi, Sina and Brandtner, Patrick and Kom{\'i}nkov{\'a} Oplatkov{\'a}, Zuzana},
  journal={MENDEL — Soft Computing Journal},
  volume={30},
  number={1},
  year={2024},
  publisher={MENDEL Journal}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Contributors

• Sina Mirshahi
• Patrick Brandtner
• Zuzana Komínková Oplatková
• Taha Falatouri
• Mehran Naseri
• Farzaneh Darbanian

🙏 Acknowledgments

This research was conducted at:

• Department of Informatics and Artificial Intelligence, Tomas Bata
• Department for Logistics, University of Applied Sciences Upper Austria, Steyr
• Josef Ressel-Centre for Predictive Value Network Intelligence, Steyr