UR2CUTE 1.2.2

A time-series forecasting model using UR2CUTE: Using Repetitively 2 CNN for Unsteady Timeseries Estimation

UR2CUTE

Using Repetitively 2 CNNs for Unsteady Timeseries Estimation. UR2CUTE is a dual-stage, PyTorch powered model dedicated to intermittent demand forecasting. A classifier estimates demand occurrence while a regressor predicts magnitude, allowing the library to focus on the sparse structure typical of slow-moving inventory.

Overview

Intermittent demand is dominated by long zero stretches punctuated by irregular spikes. Traditional statistical models struggle to capture both the timing and the size of those bursts. UR2CUTE tackles the problem with a hurdle-style architecture:

1. A CNN classifier predicts the probability of non-zero demand for each step in the forecast horizon.
2. A CNN regressor estimates the corresponding quantities.
3. Final forecasts combine the two outputs through an adaptive threshold so the regressor only contributes when demand is likely.

The estimator follows the scikit-learn API, includes thorough input validation, and automatically selects CPU or GPU devices.

Features

• Pure PyTorch implementation with GPU support when available.
• Direct multi-step forecasting: predicts the entire horizon in a single forward pass.
• Automatic lag generation plus optional external covariates.
• Customizable hyperparameters (epochs, batch size, independent learning rates, dropout).
• Auto-threshold mode that derives an occurrence cutoff from the training set.
• Early stopping with persistent checkpoints stored in a temporary directory.
• Reproducible results through explicit random seed management.
• Model persistence through save_model and load_model.
• Complete type hints and packaged type information (py.typed).

Dependencies

• Python 3.7 or newer
• PyTorch 1.7+
• NumPy
• pandas
• scikit-learn

Installation

From PyPI

pip install UR2CUTE

From Source

git clone https://github.com/FH-Prevail/UR2CUTE_torch.git
cd UR2CUTE_torch
pip install -e .

# Optional extras
pip install -e ".[dev]"
pip install -e ".[test]"
pip install -e ".[docs]"

Verify Installation

from UR2CUTE import UR2CUTE
print(UR2CUTE.__module__)

Quick Start

import pandas as pd
import torch
from UR2CUTE import UR2CUTE

data = pd.DataFrame(
    {
        "date": pd.date_range("2023-01-01", periods=50, freq="W"),
        "target": [0, 5, 0, 0, 12, 0, 0, 0, 7, 0] * 5,
        "promo": [0, 1, 0, 0, 1, 0, 0, 1, 0, 0] * 5,
        "price": [10.0, 9.5, 9.5, 9.5, 10.0, 10.0, 10.0, 9.8, 9.8, 9.8] * 5,
    }
)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

model = UR2CUTE(
    n_steps_lag=3,
    forecast_horizon=4,
    external_features=["promo", "price"],
    threshold="auto",
)
model.fit(data, target_col="target")
print(model.predict(data))

Parameters

Parameter	Description	Default
n_steps_lag	Number of lag features to generate.	3
forecast_horizon	Number of future periods predicted per call.	8
external_features	Optional list of column names used as exogenous inputs.	None
epochs	Training epochs for both CNN models.	100
batch_size	Training batch size.	32
threshold	Manual probability threshold or "auto" to derive from training data.	0.5
patience	Early stopping patience (epochs).	10
random_seed	Global random seed applied to NumPy, Python, and PyTorch.	42
classification_lr	Learning rate for the classifier.	0.0021
regression_lr	Learning rate for the regressor.	0.0021
dropout_classification	Dropout applied inside the classifier.	0.4
dropout_regression	Dropout applied inside the regressor.	0.2
verbose	Enables progress output and early-stopping logs.	True

Usage Patterns

Auto Threshold

model = UR2CUTE(threshold="auto")
model.fit(df, "target")
print(model.threshold_)

External Features

covariates = ["promotion", "price", "weekday"]
model = UR2CUTE(external_features=covariates)
model.fit(df, "target")

Silent Training

model = UR2CUTE(verbose=False)
model.fit(df, "target")

Model Persistence

trained = UR2CUTE().fit(train_df, "target")
trained.save_model("production_model.pkl")

loaded = UR2CUTE.load_model("production_model.pkl")
preds = loaded.predict(new_df)

How It Works

1. Preprocessing – validates the input frame, generates lag features, creates multi-step samples, and splits chronologically into train and validation partitions.
2. Scaling – fits separate MinMax scalers on the training set and applies them to validation and inference data, preventing validation leakage.
3. Classification Stage – trains a CNN with sigmoid output and BCE loss to estimate the probability of demand for each future horizon step.
4. Regression Stage – trains a CNN regressor with MSE loss on samples that exhibit demand; if no such sequences exist, the model safely falls back to the full dataset.
5. Inference – transforms the latest observed sequence, runs both networks, rescales quantities, and zeros out forecasts whose probability falls below the stored threshold.

Performance

Internal benchmarks show UR2CUTE outperforming Croston, AutoARIMA, Prophet, gradient boosted trees, and random forests on sparse demand series, especially in MAE% and RMSE%. Improvements stem from the dedicated occurrence model, lagged covariates, and the ability to learn temporal filters tuned to each dataset.

Citation

@article{mirshahi2024intermittent,
  title={Intermittent Time Series Demand Forecasting Using Dual Convolutional Neural Networks},
  author={Mirshahi, Sina and Brandtner, Patrick and Kominkova Oplatkova, Zuzana},
  journal={MENDEL -- Soft Computing Journal},
  volume={30},
  number={1},
  year={2024},
  publisher={MENDEL Journal}
}

License

UR2CUTE is released under the MIT License. See LICENSE for the full text.

Contributors

• Sina Mirshahi
• Patrick Brandtner
• Zuzana Kominkova Oplatkova
• Taha Falatouri
• Mehran Naseri
• Farzaneh Darbanian

Acknowledgments

This work was carried out at:

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