FinStressTS is a parametric synthetic benchmark with 30 environments across six mechanism families for evaluating point and probabilistic forecasting models on financial time series.
sktime: A Unified Interface for Machine Learning with Time Series
7 Pith papers cite this work, alongside 179 external citations. Polarity classification is still indexing.
7
Pith papers citing it
179
external citations · Pith
abstract
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct but closely related learning tasks, such as forecasting and time series classification, many of which can be solved by reducing them to related simpler tasks. We discuss the main rationale for creating a unified interface, including reduction, as well as the design of sktime's core API, supported by a clear overview of common time series tasks and reduction approaches.
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citing papers explorer
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FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance
FinStressTS is a parametric synthetic benchmark with 30 environments across six mechanism families for evaluating point and probabilistic forecasting models on financial time series.
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Unified Zero-Shot Time Series Forecasting: A Darts Foundation
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