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AIモデルライブラリ/ibm-granite/granite-timeseries-patchtst-fm-r2
モデル情報

granite-timeseries-patchtst-fm-r2

ibm-granite · granite-timeseries-patchtst-fm-r2

PatchTST-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.

翻訳待ちのため原文を表示しています。

表形式・時系列予測license:openmdw-1.0
コンテキスト長非公開
入力 → 出力待核验 → 待核验
モデルウェイト未確認
情報更新日時2026/11/2 19:15:40
OVERVIEW

モデル紹介

PatchTST-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.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

出典・チャネル入力出力課金単位収集日時
確認可能な料金がありません。出典を確認してください。

月額の基本トークン費用を試算

—

試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

収録済みの全ランキング →

よくある質問

オープンウェイトなら無料で商用利用できますか？

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか？

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

huggingface · ibm-granite/granite-timeseries-patchtst-fm-r2 ↗2026/10/29 07:41:39
huggingface · enrich:e75409d9-ea82-4850-9293-03c991032899 ↗2026/10/29 01:18:49

许可证：openmdw-1.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。