MSE-optimal multi-step forecasters cannot match the marginal distribution of realizations under nonzero conditional uncertainty, creating a quantifiable accuracy-realism Pareto frontier across benchmarks.
Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
Optimized Ridge regression with series-specific preprocessing beats prior linear forecasters and exceeds Transformer, MLP, and CNN baselines on six of eight time-series benchmarks.
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
AlphaCast is a training-free LLM framework that performs interactive multi-stage reasoning for time series forecasting by integrating feature extraction, knowledge bases, case libraries, and contextual pools.
PMDformer uses patch-mean decoupling, trend restoration attention, and proximal variable attention to improve accuracy and stability in long-term time series forecasting benchmarks.
citing papers explorer
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Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
MSE-optimal multi-step forecasters cannot match the marginal distribution of realizations under nonzero conditional uncertainty, creating a quantifiable accuracy-realism Pareto frontier across benchmarks.
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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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Deep Time Series Models: A Comprehensive Survey and Benchmark
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
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How Good Can Linear Models Be for Time-Series Forecasting?
Optimized Ridge regression with series-specific preprocessing beats prior linear forecasters and exceeds Transformer, MLP, and CNN baselines on six of eight time-series benchmarks.
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NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting
AlphaCast is a training-free LLM framework that performs interactive multi-stage reasoning for time series forecasting by integrating feature extraction, knowledge bases, case libraries, and contextual pools.
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PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting
PMDformer uses patch-mean decoupling, trend restoration attention, and proximal variable attention to improve accuracy and stability in long-term time series forecasting benchmarks.