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Position Paper: An Integrated Perspective on Data, Metrics, and Methodology for Deep Time-Series Forecasting
Deep time-series forecasting plays an integral role in numerous practical applications.
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
Towards Foundation Models for Learning on Tabular Data
Despite considerable efforts in developing effective learning models for tabular data, current transferable tabular models remain in their infancy, limited by either the lack of support for direct instruction following in new tasks or the neglect of acquiring foundational knowledge and capabilities from diverse tabular datasets.
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