Contamination Map Methodology

Time series / Model

Moirai-2.0-R

Organisation
Salesforce AI Research
Family
Moirai · version 2.0-R
Released
2025-08-06 (HF repo creation of Salesforce/moirai-2.0-R-small (HF API createdAt 2025-08-06); uni2ts README: 'Aug 2025: Released Moirai-2.0-R-small'; paper arXiv 2511.11698v1 submitted 2025-11-12)
Checkpoints
Salesforce/moirai-2.0-R-small
Weights
Open · cc-by-nc-4.0 (weights, per HF model card); uni2ts code is Apache-2.0
Links
PaperModel cardCode

Training data disclosure: Partial list

Level determined for 73% of benchmark cells.

“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“A core component of our pre-training corpus is derived from internal salesforce telemetry data.”
arxiv.org · paper · Section 4, Internal Salesforce data · retrieved 2026-10-15
“We only use non leaking subset of datasets from Chronos data to generate the mixup to keep our training free of leakage.”
arxiv.org · paper · Section 4, Chronos-Mixup · retrieved 2026-10-15

Training corpora

Other declared training data

Declared exclusions

Benchmark cells

GIFT-Eval

Training data seen Jena Weather (GIFT-Eval) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen BizITObs - Application · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen BizITObs - Service · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen BizITObs - L2C · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Bitbrains - Fast Storage · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Bitbrains - rnd · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Restaurant (Recruit Restaurant Visitor Forecasting) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen ETT1 (GIFT-Eval) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen ETT2 (GIFT-Eval) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Loop Seattle (LibCity) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen SZ-Taxi (LibCity) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen M_DENSE (LibCity) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Solar (LSTNet, 137 Alabama PV plants) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Hierarchical Sales (Mancuso et al., 2020) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen M4 Yearly · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen M4 Quarterly · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen M4 Monthly · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen M4 Weekly · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen M4 Daily · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen M4 Hourly · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Hospital · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen COVID Deaths · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen US Births · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Saugeen River Flow · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Temperature Rain · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen KDD Cup 2018 · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen Car Parts · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Training data seen ElectricityLoadDiagrams20112014 (UCI) · only the training split was used, not the benchmark's test data; the test portion was explicitly excluded

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“large and diverse set of time series carefully curated to avoid overlap with benchmark evaluation tasks.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15

Long-horizon forecasting (ETT, Electricity, Traffic, Weather, Exchange, ILI)

Related data seen ETTh1 · related dataset ETT1 (GIFT-Eval) was used

Evidence (4)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“From Informer (Zhou et al., 2021) we utilize ETT1 and ETT2 datasets, which denote electricity transformer temperature”
arxiv.org · paper · Appendix D · retrieved 2026-10-15
“ETTh1&ETTh2 7 17,420”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Related data seen ETTh2 · related dataset ETT2 (GIFT-Eval) was used

Evidence (4)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“From Informer (Zhou et al., 2021) we utilize ETT1 and ETT2 datasets, which denote electricity transformer temperature”
arxiv.org · paper · Appendix D · retrieved 2026-10-15
“ETTh1&ETTh2 7 17,420”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Related data seen ETTm1 · related dataset ETT1 (GIFT-Eval) was used

Evidence (4)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“From Informer (Zhou et al., 2021) we utilize ETT1 and ETT2 datasets, which denote electricity transformer temperature”
arxiv.org · paper · Appendix D · retrieved 2026-10-15
“ETTm1 &ETTm2 7 69,680”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Related data seen ETTm2 · related dataset ETT2 (GIFT-Eval) was used

Evidence (4)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“From Informer (Zhou et al., 2021) we utilize ETT1 and ETT2 datasets, which denote electricity transformer temperature”
arxiv.org · paper · Appendix D · retrieved 2026-10-15
“ETTm1 &ETTm2 7 69,680”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Related data seen Electricity (ECL, 321 clients) · related dataset ElectricityLoadDiagrams20112014 (UCI) was used

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“The raw dataset is in https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014.”
raw.githubusercontent.com · readme · Electricity consumption · retrieved 2026-10-15
“Because the some dimensions are equal to 0. So we eliminate the records in 2011. Final we get data contains electircity consumption of 321 clients from 2012 to 2014.”
raw.githubusercontent.com · readme · Electricity consumption · retrieved 2026-10-15
“https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014”
arxiv.org · paper · Section 4, footnote 1 · retrieved 2026-10-15

Seen, split unclear Traffic (PeMS San Francisco Bay, 862 sensors) · the dataset was used; the source does not say which part (via GIFT-Eval Pretrain (GiftEvalPretrain))

Evidence (4)
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“Our new pretraining corpus also includes this same non-leaking dataset as its foundation.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“Traffic Hourly”
arxiv.org · paper · Table 14 (dataset column) · retrieved 2026-10-15

Related data seen Weather (Jena, 2020; Autoformer) · related dataset Jena Weather (GIFT-Eval) was used

Evidence (5)
“Additionally we add the train split of G IFT-E VAL T RAIN T EST dataset to train Moirai 2.0, which includes 144K time series.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“We incorporate Jena Weather5 dataset following Autoformer (Wu et al., 2021).”
arxiv.org · paper · Appendix D (footnote 5 points to https://www.bgc-jena.mpg.de/wetter/) · retrieved 2026-10-15
“is recorded every 10 minutes for 2020 whole year, which contains 21 meteorological indicators, such as air temperature, humidity, etc.”
arxiv.org · paper · Section 4 (Datasets) · retrieved 2026-10-15
“https://www.bgc-jena.mpg.de/wetter/”
arxiv.org · paper · Section 4, footnote 3 · retrieved 2026-10-15

Unknown Exchange-Rate · training data not disclosed in enough detail

Related data seen ILI (CDC influenza-like illness, national) · related dataset CDC FluView ILINet (LOTSA) was used (via GIFT-Eval Pretrain (GiftEvalPretrain))

Evidence (7)
“Our pretraining corpus is constructed from five complementary sources: (1) the non-leaking G IFT-E VAL P RETRAIN dataset, (2) train split of G IFT-E VAL T RAIN T EST dataset [1] (3) additional series generated via Chronos-Mixup, (4) KernelSynth data [2], and (5) anonymized internal Salesforce CloudOps telemetry data.”
arxiv.org · paper · Section 4, Pretraining Datasets · retrieved 2026-10-15
“Our new pretraining corpus also includes this same non-leaking dataset as its foundation.”
arxiv.org · paper · Section 4, Gift-Eval Pretrain and Gift-Eval TrainTest · retrieved 2026-10-15
“Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).”
huggingface.co · model card · retrieved 2026-10-15
“CDC Fluview ILINet”
arxiv.org · paper · Table 14 (dataset column) · retrieved 2026-10-15
“includes the weekly recorded influenza-like illness (ILI) patients data from Centers for Disease Control and Prevention of the United States between 2002 and 2021”
arxiv.org · paper · Section 4 (Datasets) · retrieved 2026-10-15
“https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html”
arxiv.org · paper · Section 4, footnote 4 · retrieved 2026-10-15
“CDC Fluview ILINet CDC Fluview WHO NREVSS”
arxiv.org · paper · Table 14 (dataset column) · retrieved 2026-10-15

Time-series anomaly detection

Unknown TSB-AD-U (TSB-AD univariate) · training data not disclosed in enough detail

Unknown TSB-AD-M (TSB-AD multivariate) · training data not disclosed in enough detail

Unknown UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021) · training data not disclosed in enough detail

Unknown Server Machine Dataset (SMD) · training data not disclosed in enough detail

Unknown Mars Science Laboratory (MSL) telemetry anomaly dataset · training data not disclosed in enough detail

Unknown Soil Moisture Active Passive (SMAP) telemetry anomaly dataset · training data not disclosed in enough detail

Unknown Pooled Server Metrics (PSM) · training data not disclosed in enough detail

Unknown Secure Water Treatment (SWaT) · training data not disclosed in enough detail

Unknown Yahoo S5 Labeled Anomaly Detection Dataset (Yahoo Webscope S5) · training data not disclosed in enough detail

Unknown Numenta Anomaly Benchmark (NAB) · training data not disclosed in enough detail

Time-series classification

Unknown UCR Time Series Classification Archive (2018) · training data not disclosed in enough detail

Unknown UEA Multivariate Time Series Classification Archive (2018) · training data not disclosed in enough detail

Notes

Decoder-only, quantile loss. Total ~36M series / ~295B observations. Only the small checkpoint is public; the paper also trained base and large variants that underperformed small. Trained on the GIFT-Eval train split (in-distribution w.r.t. GIFT-Eval test windows) but test windows excluded. The paper notes GIFT-Eval Pretrain is a subset of LOTSA.

Sources