tsmorph 0.1.1

A package for generating semi-synthetic time series using morphing techniques.

tsMorph

tsMorph is a Python package designed to generate semi-synthetic time series through morphing techniques. It enables the systematic transformation between two given time series, facilitating robust performance evaluation of forecasting models.

This package is based on the paper:
Santos, M., de Carvalho, A., & Soares, C. (2024). Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation. arXiv:2312.01344

Features

• Generation of Semi-Synthetic Time Series: Creates a set of intermediate time series transitioning from a source series (S) to a target series (T).
• Performance Understanding: Evaluates forecasting models' robustness using MASE (Mean Absolute Scaled Error) over synthetic series.
• Feature Extraction: Uses pycatch22 to extract time series features for deeper analysis.
• Visualization Tools: Provides plotting functions to explore synthetic time series and their performance.

Installation

pip install tsmorph

Usage

Generate Semi-Synthetic Time Series

import numpy as np
import pandas as pd
from tsmorph import TSmorph

# Define source and target time series
S = np.array([1, 2, 3, 4, 5])
T = np.array([6, 7, 8, 9, 10])

ts_morph = TSmorph(S, T, granularity=5)
synthetic_df = ts_morph.fit()
print(synthetic_df)

Plot Semi-Synthetic Time Series

ts_morph.plot(synthetic_df)

Performance Understanding with Forecasting Models

from some_forecasting_model import TrainedModel

# Assume a trained forecasting model compatible with NeuralForecast
model = TrainedModel()

# Define forecast horizon
horizon = 2

# Analyze performance over synthetic series
ts_morph.analyze_morph_performance(synthetic_df, model, horizon)

Citation

If you use tsMorph in your research, please cite:

@article{santos2024tsmorph,
  title={Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation},
  author={Santos, Mois{\'e}s and de Carvalho, Andr{\'e} and Soares, Carlos},
  journal={arXiv preprint arXiv:2312.01344},
  year={2024}
}

License

This project is licensed under the GNU General Public License v3.0.