time-series-transformer-tourism-monthly
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granite-timeseries-ensemble-r1
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詳細を見る ↗granite-timeseries-flowstate-r1
ibm-granite**Important**: To use FlowState-r1.1 include the `revision=r1.1` argument when loading the model, as demonstrated in the `Recommended Use` section. If no `revision` argument is provided, the version 1.0 is used. - **FlowState**: We present an SSM-based time series foundation model that can be dynamically adjusted to the specific characteristics of the time series during evaluation. - **Functional Basis Decoder (FBD)**: We propose a novel decoder, as a critical component of FlowState, that utilizes a set of continuous basis functions to make continuous forecasts and allow seamless adjustment to specific input characteristics. - **Flexible temporal adaptation**: FlowState can dynamically adjust the context and target length to the timescale of the provided time series. - **Compact and high-performing**: With fewer than 10M parameters and the ability to forecast multiple consecutive patches in parallel, FlowState delivers state-of-the-art accuracy with exceptional efficiency. This model card contains the model weights for the Granite version of the FlowState model, which is suitable for commercial and enterprise use. A research version of the model (available here) is also provided and is intended for research use only. The Granite and research versions achieve the same MASE performance on GIFT‑Eval, indicating comparable point forecasting accuracy. The research version demonstrates slightly improved CRPS performance, which evaluates the quality of probabilistic forecasts. Despite being **more than 10x smaller** than the next best models, FlowState is **better than all Zero-Shot models** with publicly available source code and model weights on the GIFT-Eval Leaderboard in terms of MASE performance, as of Apr. 28th 2026. The Figure compares GIFT MASE Performance vs. model size for FlowState and the most competitive Zero-Shot Models.
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詳細を見る ↗granite-timeseries-patchtsmixer
ibm-graniteThe `PatchTSMixer` is a lightweight and fast multivariate time series forecasting model with state-of-the-art performance on benchmark datasets. In this context, we offer a pre-trained `PatchTSMixer` model encompassing all seven channels of the `ETTh1` dataset. This particular pre-trained model produces a Mean Squared Error (MSE) of 0.37 on the `test` split of the `ETTh1` dataset when forecasting 96 hours into the future with a historical data window of 512 hours. The PatchTSMixer model was proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam. PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. In this HuggingFace implementation, we provide PatchTSMixer’s capabilities to effortlessly facilitate lightweight mixing across patches, channels, and hidden features for effective multivariate time-series modeling. It also supports various attention mechanisms starting from simple gated attention to more complex self-attention blocks that can be customized accordingly. The model can be pretrained and subsequently used for various downstream tasks such as forecasting, classification and regression.
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詳細を見る ↗granite-timeseries-patchtst
ibm-granite`PatchTST` is a transformer-based model for time series modeling tasks, including forecasting, regression, and classification. This repository contains a pre-trained `PatchTST` model encompassing all seven channels of the `ETTh1` dataset. This particular pre-trained model produces a Mean Squared Error (MSE) of 0.3881 on the `test` split of the `ETTh1` dataset when forecasting 96 hours into the future with a historical data window of 512 hours. The `PatchTST` model was proposed in A Time Series is Worth 64 Words: Long-term Forecasting with Transformers by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. At a high level the model vectorizes time series into patches of a given size and encodes the resulting sequence of vectors via a Transformer that then outputs the prediction length forecast via an appropriate head.
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詳細を見る ↗granite-timeseries-patchtst-fm-r1
ibm-graniteThe architecture incorporates the following changes: - residual blocks in the input and output projections - a quantile head to support probabilistic forecasting - enhanced training strategies incorporating contiguous patch masking, and random masking in the forecast period - trained with reconstruction-loss objective - forecast at inference time is cast as "reconstruction" of the masked forecast period while past context is not masked. If the context period has missing values, both the missing timepoints and forecast timepoints are filled in --- thus providing simultaneous imputation and forecast capability. The particular model here was trained on a diverse set of data (see details below) using significantly longer context length than done previously (8192). Here the context includes both the input context as well as the length of the desired forecast horizon. Shorter input series are pre-pended with the mean of the series values in the context and the padded regions are treated as masked. The d_model is set to 1024, patch_length is 16, and we train the quantile head with 99 quantiles. In total, the model has ~260M parameters, ~250M of which are in the core transformer layers. The model architecture is based on a new implementation of a PatchTST-style architecture, which is available in the IBM TSFM repository. Other implementations of PatchTST can be found here: - Official HuggingFace implementation: Contributed by members of the IBM TSFM team. - Original PatchTST Implementation: Implemented by an IBM Research intern who was part of the IBM TSFM team, and the first author of the original PatchTST paper.
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詳細を見る ↗granite-timeseries-patchtst-fm-r2
ibm-granitePatchTST-FM-r2, a state-of-the-art zero-shot time series foundation model, represents a continuation of the well-recognized PatchTST model series, building on the original PatchTST and its zero-shot variant PatchTST-FM-r1. PatchTST-FM-r2 brings architectural enhancements as well as an expanded training base on top of its predecessor PatchTST-FM-r1. As of August 31, 2026 Granite-TimeSeries-PatchTST-FM-r2 is the **top performing** zero-shot model released under a permissive, commercial-friendly open-source license on the GIFT-Eval benchmark. Granite-TimeSeries-PatchTST-FM-r2 ranks #2 when considering all zero-shot, replicable models (see below for more details). The architectural changes in r2 include: - Introduction of a convolution layer after the multi-head self-attention (MHSA) layer, sandwiched between two "half-step" FFNs. This block, known as the Conformer block, previously gained popularity in speech recognition. The convolution sublayer tends to capture near-term information between patches thus enabling the MHSA to focus on long-term relationships. - Plain-transformer blocks are replaced by Conformer blocks with alternating convolution kernel sizes of 3 and 5, in a repeating pattern {5, 5, 3, 3} - Overlapping patches of size 16 with stride of 8, combined with a Hamming window loss weighting at training time, and an overlap-and-add forecasting approach at inference time - The overall number of blocks increased to 30 (from 20) to absorb expanded training data - A pre-head layer norm is applied for enhanced training stability - Training is expanded by synthetic data generated using the CauKer method, as used in the training of the Granite-FlowState model As with PatchTST-FM-r1, the r2 model was trained on a diverse set of data (see details below) using context length of 8192. The model hidden dimension is set to 1024, patch_length is 16, and the quantile head spans 99 quantiles. In total, the model has ~385M parameters.
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詳細を見る ↗granite-timeseries-ttm-r1
ibm-graniteTinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With less than 1 Million parameters, TTM (accepted in NeurIPS 24) introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our paper for more details. **The current open-source version supports point forecasting use-cases specifically ranging from minutely to hourly resolutions (Ex. 10 min, 15 min, 1 hour.).**
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詳細を見る ↗granite-timeseries-ttm-r2
ibm-graniteTinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With model sizes starting from 1M params, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting. The paper describing TTM was accepted at NeurIPS 24.** TTM outperforms other models demanding billions of parameters in several popular zero-shot and few-shot forecasting benchmarks. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed on 1 GPU or on laptops.** TTM r2 comprises TTM variants pre-trained on larger pretraining datasets (\~700M samples). The TTM r2.1 release increases the pretraining dataset size to approximately (\~1B samples). The prior model releases, TTM r1, were trained on \~250M samples and can be accessed here. In general, TTM r2 models perform better than TTM r1 models as they are trained on a larger pretraining dataset. In standard benchmarks, TTM r2 outperform TTM r1 by over 15%. However, the choice of r1 vs. r2 depends on your target data distribution, and hence users should try both variants and pick the best model for your data. The TTM r2 releases support point forecasting use-cases specifically ranging from minutely to hourly resolutions (Ex. 10 min, 15 min, 1 hour.). With the TTM r2.1 release, we add support for daily and weekly resolutions.
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詳細を見る ↗granite-timeseries-ttm-r3
ibm-graniteBuilding on top of **TTM-R1** and **TTM-R2**, we introduce the next generation of TinyTimeMixer under the Granite time-series foundation model family — **Granite-TTM-R3**. This release incorporates several novel *tiny-neural architectural innovations* designed to push the limits of **accuracy in high-speed forecasting**, a critical requirement for real-world production deployments. **Granite-TTM-R3** is a family of pretrained models supporting multiple real-world forecasting scenarios: - **Zero-shot forecasting** across unseen datasets - **Few-shot adaptation** effective with as few as ~1K samples - **Full fine-tuning** for domain-specific optimization - **Multivariate time-series forecasting** - **Exogenous / control variable integration** - **High-throughput batch inference** for production systems
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詳細を見る ↗flowstate
ibm-research**Important**: To use FlowState-r1.1 include the `revision=r1.1` argument when loading the model, as demonstrated in the `Recommended Use` section. If no `revision` argument is provided, the version 1.0 is used. - **FlowState**: We present an SSM-based time series foundation model that can be dynamically adjusted to the specific characteristics of the time series during evaluation. - **Functional Basis Decoder (FBD)**: We propose a novel decoder, as a critical component of FlowState, that utilizes a set of continuous basis functions to make continuous forecasts and allow seamless adjustment to specific input characteristics. - **Flexible temporal adaptation**: FlowState can dynamically adjust the context and target length to the timescale of the provided time series. - **Compact and high-performing**: With fewer than 10M parameters and the ability to forecast multiple consecutive patches in parallel, FlowState delivers state-of-the-art accuracy with exceptional efficiency. This model card contains the model-weights for research-use only and full reproducibility of our results published in our paper. However - if you are looking for the FlowState model weights for commercial and enterprise use, please refer to our granite releases here Despite being **more than 10x smaller** than the next best models, FlowState is the **best Zero-Shot model** with publicly available source code and model weights on the GIFT-Eval Leaderboard, as of Mar. 16th 2026. The Figure compares GIFT MASE Performance vs. model size for FlowState and the most competitive Zero-Shot Models. Model Details can be found in our Paper. Currently FlowState only supports zero-shot forecasting. FlowState can be used to make predictions as follows:
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詳細を見る ↗patchtst-etth1-pretrain
ibm-research基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗patchtst-fm-r1
ibm-researchThis model card is for the non-commercial, research version of PatchTST-FM-r1. Please also check-out the Apache-2.0 licensed IBM Granite version. The architecture incorporates the following changes: - residual blocks in the input and output projections - a quantile head to support probabilistic forecasting - enhanced training strategies incorporating contiguous patch masking, and random masking in the forecast period - trained with reconstruction-loss objective - forecast at inference time is cast as "reconstruction" of the masked forecast period while past context is not masked. If the context period has missing values, both the missing timepoints and forecast timepoints are filled in --- thus providing simultaneous imputation and forecast capability. The particular model here was trained on a diverse set of data (see details below) using significantly longer context length than done previously (8192). Here the context includes both the input context as well as the length of the desired forecast horizon. Shorter input series are pre-pended with the mean of the series values in the context and the padded regions are treated as masked. The d_model is set to 1024, patch_length is 16, and we train the quantile head with 99 quantiles. In total, the model has ~260M parameters, ~250M of which are in the core transformer layers.
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詳細を見る ↗test-patchtsmixer
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詳細を見る ↗test-patchtst
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詳細を見る ↗test-ttm-v1
ibm-research基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗ttm-r3
ibm-research基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗ttm-research-r2
ibm-researchTinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With model sizes starting from 1M params, TTM (accepted in NeurIPS 24) introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** This model card contains the model-weights for research-use only and full reproducibility of our results published in our paper. However - if you are looking for TTM model weights for commercial and enterprise use, please refer to our granite releases here TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our paper for more details.
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詳細を見る ↗skops-digits
julien-c基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗EXAONE-Forecast-for-Finance-1.0
LG-AI-Research基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗EXAONE-Tabular
LG-AI-Research**EXAONE Tabular** is a transformer-based **foundation model for tabular data** that solves **classification** and **regression** through **in-context learning**: you pass the labeled rows to `fit` and the model predicts new rows in a single forward pass — **no gradient updates and no per-dataset training**. This repository is the **`exaonetabular` inference runtime** — a self-contained package that loads a released checkpoint and serves predictions through a small, scikit-learn-style API. The code here is permissively licensed; the released **weights are non-commercial** — see License. Both checkpoints are released: `EXAONETabularClassifier` and `EXAONETabularRegressor` each fetch their own weights with a single `from_pretrained()` call. See Available checkpoints.
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詳細を見る ↗TimeMoE-200M
Maple728基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗TimeMoE-50M
Maple728This repository contains the weights of the TimeMoE-50M model of the paper Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.
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Timestral-123M
Mistral AITimestral is a **family of time series foundation models** for zero-shot forecasting, available in **5M**, **28M**, **123M**, and **209M** parameter versions. This repository contains the 123M checkpoint. >[!Note] > For more information about our Timestral family, go to the main model page: Timestral-209M. Timestral-209M achieves **state-of-the-art zero-shot accuracy** across three major forecasting benchmarks: GIFT-Eval, TIME, and fev-bench.
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