Olivia harmonizes time series datasets via normalized power spectral density using a Harmonizer module and resonator-based HarmonicAttention, achieving state-of-the-art zero-shot, few-shot, and full-shot forecasting on TSLib, GIFT-Eval, and GluonTS benchmarks.
Timer: Generative pre-trained transformers are large time series models
13 Pith papers cite this work. Polarity classification is still indexing.
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TimeClaw is an exploratory execution learning system that turns multiple valid tool-use paths into hierarchical distilled experience for improved time-series reasoning without test-time adaptation.
TimeTok is a unified framework using hierarchical tokenization for granularity-controllable time-series generation that achieves state-of-the-art performance in standard tasks and shows transferability across heterogeneous datasets.
Super-Linear introduces a pretrained MoE architecture using frequency-specialized linear experts and spectral gating for efficient general time series forecasting.
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.
Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
Tyan-WP is a pretrained wind power foundation model that outperforms site-specific TSMs and generic LTSMs in zero-shot ultra-short-term probabilistic forecasting on U.S. and U.K. sites via static embeddings and PAMF module.
TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and sentiment benchmarks under missingness.
Tabular foundation models applied to PHM via signal-to-table conversion achieve the best average ranks across prognostic and diagnostic tasks and remain competitive in low-data regimes.
CondI applies conditional diffusion models in a two-phase federated pipeline to impute within-modality missing data, then trains extractors on the completed inputs for downstream tasks on clinical datasets.
TelecomTS is a de-anonymized 5G observability benchmark with scale information, anomalies, and multi-modal QA tasks, on which current foundation models perform poorly.
PMDformer uses patch-mean decoupling, trend restoration attention, and proximal variable attention to improve accuracy and stability in long-term time series forecasting benchmarks.
Normalization choice significantly influences training convergence and forecasting performance in causal large time-series models.
citing papers explorer
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Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density
Olivia harmonizes time series datasets via normalized power spectral density using a Harmonizer module and resonator-based HarmonicAttention, achieving state-of-the-art zero-shot, few-shot, and full-shot forecasting on TSLib, GIFT-Eval, and GluonTS benchmarks.
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TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
TimeClaw is an exploratory execution learning system that turns multiple valid tool-use paths into hierarchical distilled experience for improved time-series reasoning without test-time adaptation.
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TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
TimeTok is a unified framework using hierarchical tokenization for granularity-controllable time-series generation that achieves state-of-the-art performance in standard tasks and shows transferability across heterogeneous datasets.
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Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting
Super-Linear introduces a pretrained MoE architecture using frequency-specialized linear experts and spectral gating for efficient general time series forecasting.
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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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Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
Compares foundation models for probabilistic low-voltage load forecasting on 200 real feeders and introduces a grid-planning metric that scores peak prediction by its effect on asset cost-risk decisions.
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Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
Tyan-WP is a pretrained wind power foundation model that outperforms site-specific TSMs and generic LTSMs in zero-shot ultra-short-term probabilistic forecasting on U.S. and U.K. sites via static embeddings and PAMF module.
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TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and sentiment benchmarks under missingness.
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Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models
Tabular foundation models applied to PHM via signal-to-table conversion achieve the best average ranks across prognostic and diagnostic tasks and remain competitive in low-data regimes.
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Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
CondI applies conditional diffusion models in a two-phase federated pipeline to impute within-modality missing data, then trains extractors on the completed inputs for downstream tasks on clinical datasets.
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TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
TelecomTS is a de-anonymized 5G observability benchmark with scale information, anomalies, and multi-modal QA tasks, on which current foundation models perform poorly.
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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.
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Does Normalization Choice Matter for Causal Large Time-Series Models?
Normalization choice significantly influences training convergence and forecasting performance in causal large time-series models.