<ul><li>Developments in Deep Learning have improved time series forecasting by modeling complex temporal dependencies.</li><li>Research aims to find connections between time series characteristics and model strengths.</li><li>A new dataset using Gaussian Processes shows data characteristics for model evaluations.</li><li>Introduction of TimeFlex model tailored for handling diverse temporal dynamics compared to existing models.</li></ul>

Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data

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