TempusBench is a new evaluation framework for time-series forecasting models that supplies fresh non-overlapping datasets, tasks beyond horizon and domain, consistent tuning across models, and visualization tools.
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DyWPE generates positional embeddings for time series transformers from the input signal via Discrete Wavelet Transform and outperforms standard positional encodings on ten datasets, especially longer sequences and biomedical signals.
A survey of positional encoding methods in transformer-based time series models that evaluates fixed, learnable, relative, and hybrid approaches on classification tasks and links effectiveness to data characteristics.
citing papers explorer
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TempusBench: An Evaluation Framework for Time-Series Forecasting
TempusBench is a new evaluation framework for time-series forecasting models that supplies fresh non-overlapping datasets, tasks beyond horizon and domain, consistent tuning across models, and visualization tools.
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DyWPE: Signal-Aware Dynamic Wavelet Positional Encoding for Time Series Transformers
DyWPE generates positional embeddings for time series transformers from the input signal via Discrete Wavelet Transform and outperforms standard positional encodings on ten datasets, especially longer sequences and biomedical signals.
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Positional Encoding in Transformer-Based Time Series Models: A Survey
A survey of positional encoding methods in transformer-based time series models that evaluates fixed, learnable, relative, and hybrid approaches on classification tasks and links effectiveness to data characteristics.