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Introducing TFB: An Open-Source Machine Learning Library for Time Series Researchers
Robust benchmarks are essential for evaluating new methods across diverse datasets. TFB addresses limitations in Time Series Forecasting (TSF) benchmarks by offering a curated collection of realistic datasets, fostering fair evaluations and comparisons among methodologies.
Key Features of TFB
- Broad coverage of statistical learning, machine learning, and deep learning methods
- Flexible and scalable pipeline for fair method comparisons
Through TFB, researchers gain practical insights into the performance of different TSF methods across diverse datasets and characteristics, enabling more robust comparisons among competing methodologies.
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