Pith. sign in

REVIEW 22 cited by

MOMENT: A Family of Open Time-series Foundation Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.03885 v3 pith:22PYNDU6 submitted 2024-02-06 cs.LG cs.AI

Mononito Goswami , Konrad Szafer , Arjun Choudhry , Yifu Cai , Shuo Li , Artur Dubrawski This is my paper
classification cs.LGcs.AI
keywords timeseriesmodelslargediversefoundationpre-trainedautonlab
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2) diverse time series characteristics which make multi-dataset training onerous. Additionally, (3) experimental benchmarks to evaluate these models, especially in scenarios with limited resources, time, and supervision, are still in their nascent stages. To address these challenges, we compile a large and diverse collection of public time series, called the Time series Pile, and systematically tackle time series-specific challenges to unlock large-scale multi-dataset pre-training. Finally, we build on recent work to design a benchmark to evaluate time series foundation models on diverse tasks and datasets in limited supervision settings. Experiments on this benchmark demonstrate the effectiveness of our pre-trained models with minimal data and task-specific fine-tuning. Finally, we present several interesting empirical observations about large pre-trained time series models. Pre-trained models (AutonLab/MOMENT-1-large) and Time Series Pile (AutonLab/Timeseries-PILE) are available on Huggingface.

Discussion (0). Sign in to comment.

Forward citations

Cited by 22 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

    cs.LG 2026-07 conditional novelty 7.0 of 10

    A multi-vehicle naturalistic benchmark finds learned driving embeddings retain driver identity under condition matching, while descriptors collapse and video re-ID is mostly route leakage.

  2. AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection

    cs.LG 2026-02 conditional novelty 7.0 of 10

    New RL approach (TimerPO) with ground-truth-generated expert reasoning traces lets 3B-7B multimodal LLMs outperform GPT-4o on time-series anomaly detection and explanation.

  3. LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers

    cs.LG 2026-07 conditional novelty 6.0 of 10

    A whole-log transformer with a geology-aware loss predicts stratigraphic zones and marker depths, beating sliding-window baselines on three well-log datasets.

  4. Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule

    stat.AP 2026-07 conditional novelty 6.0 of 10

    Zero-shot Chronos wins time-series benchmarks by under-extrapolating trend, and trend strength computed before forecasting predicts when it will beat classical models.

  5. Learning Spatio-Temporal Foundation Models from Pure Synthetic Data

    cs.LG 2026-06 conditional novelty 6.0 of 10

    A spatio-temporal foundation model pre-trained exclusively on synthetic stochastic graph dynamics outperforms real-data-pretrained STFMs in zero-shot traffic forecasting, according to the paper's benchmarks.

  6. TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models

    cs.LG 2026-01 conditional novelty 6.0 of 10

    TimeSAE trains a sparse autoencoder with counterfactual and consistency losses to explain black-box time series predictions, claiming better faithfulness and out-of-distribution robustness than eight baselines.

  7. WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting

    cs.LG 2025-09 conditional novelty 6.0 of 10

    A wind-specific foundation model, WindFM, uses hierarchical tokenization and autoregressive pre-training on the NREL WIND Toolkit to achieve state-of-the-art zero-shot wind power forecasts.

  8. CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams

    cs.LG 2025-08 reject novelty 6.0 of 10

    CALM uses an LLM-as-a-Judge to curate anomalies for continuous fine-tuning of a time-series foundation model, improving anomaly detection on held-out stream segments.

  9. Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Pre-trained multivariate time-series imputation models frequently return values that violate known relations between variables, and a diffusion-based score can detect and filter these errors.

  10. Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

    cs.LG 2025-06 unverdicted novelty 6.0 of 10

    Time-R1 trains LLMs via supervised fine-tuning followed by reinforcement learning with a time-series-specific reward and non-uniform GRIP sampling to enable multi-step reasoning that improves forecasting accuracy.

  11. Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions

    cs.LG 2025-05 reject novelty 6.0 of 10

    CHARM is a 7M-parameter self-supervised embedding model for multivariate time series that uses channel descriptions to beat specialized baselines on forecasting, classification, and anomaly detection.

  12. Benchmarking Foundation Models with Multimodal Public Electronic Health Records

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A standardized MIMIC-IV benchmark comparing eight unimodal and multimodal foundation models shows multimodal inputs improve predictive performance without adding bias, while medical LVLMs underperform on length-of-sta...

  13. Towards Interpretable Time Series Foundation Models

    cs.CL 2025-07 conditional novelty 5.0 of 10

    After fine-tuning on 180 synthetic mean-reverting series annotated by a large multimodal model, small Qwen models can describe trend direction, noise intensity, and extremum location in natural language.

  14. Causal Foundation Models: Disentangling Physics from Instrument Properties

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A dual-encoder contrastive model trained on star-instrument triplets learns separate stellar and instrumental latent spaces, improving few-shot prediction of stellar parameters in simulated TESS-like light curves.

  15. Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

    cs.LG 2025-06 reject novelty 5.0 of 10

    TimesCLIP aligns image-based and text-based views of the same time series via contrastive learning to improve forecasting accuracy on several benchmarks, but the full multimodal model is not used on two of the six lon...

  16. Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models

    eess.IV 2025-06 conditional novelty 5.0 of 10

    Using gradient saliency and pretrained video/time-series models, frame-level stroke exercise quality can be learned from video-level labels, with the best configuration reaching 72% AUC versus 69% for a ground-truth-t...

  17. Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition

    cs.LG 2026-01 conditional novelty 4.0 of 10

    Test-time adaptation—adaptive batch norm, replay-regularized feature alignment, and few-shot meta-learning—improves NinaPro DB6 inter-session EMG gesture accuracy from ~57% baseline to ~69–70% unsupervised and ~80% wi...

  18. On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating

    cs.LG 2025-08 conditional novelty 4.0 of 10

    On four public datasets, Gradient Boosting with hand-built features beat Chronos, Llama, and ARIMA on most accuracy metrics, while Chronos only led on financial sMAPE.

  19. MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models

    cs.LG 2025-07 conditional novelty 4.0 of 10

    MoFE-Time reports average MSE 0.2755 and MAE 0.3226 across six public benchmarks, about 7% lower than Time-MoE, by adding frequency-domain experts to a Mixture of Experts transformer.

  20. Grounding Intelligence in Movement

    cs.AI 2025-07 conditional novelty 4.0 of 10

    Movement should be treated as a first-class AI modeling modality, and a unified, biomechanically grounded movement foundation model built from aggregated data across species and sensors is the proposed path forward.

  21. Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Delayformer embeds each time series variable into a Hankel matrix, processes the matrices as images with a shared ViT, and predicts all variables in parallel.

  22. Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives?

    cs.LG 2025-06 reject novelty 3.0 of 10

    LLM4TS_FS achieves the best MSE on four of seven long-term datasets, but the claimed broad advantage of pre-trained large models over small transformers is not consistent across all benchmarks.

Pith tools