datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Time-Series-Library
Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current… See the full description on the dataset page: https://huggingface.co/datasets/thuml/Time-Series-Library.time-series-datasettimeseries_datasynthetic-timeseries-data
cruscy data — evaluation sample
Three full days of real crypto market microstructure (Binance spot), prepared
for public evaluation: absolute prices, dates, and the instrument are withheld —
the shape of the day (tick-by-tick relative price, normalized volumes, trade
side, book imbalance) is fully preserved.
The full feed — 27+ streams (raw L2 depth, 1-second trade tape, order-book
metrics, derived features, regime labels) with SQL console, backtest runner and
MCP access for AI… See the full description on the dataset page: https://huggingface.co/datasets/GOD111111111/synthetic-timeseries-data.timeseries-daily-stocks
Dataset Card for "timeseries-daily-stocks"
More Information needed
wikimedia-pageview-timeseries-raw
Wikimedia Pageview Time Series — full raw (wide format)
Full, unsampled Wikipedia pageview time series for every Wikimedia
project (Wikipedia, Wiktionary, Commons, etc.), stored as raw wide
parquet files: one row per article, one column per timestamp.
This is the complete derived output of the upstream pipeline —
the companion repo
jeremycochoy/wikimedia-pageview-timeseries
holds a sampled, reshaped version (3.7 M rows in HF long format
for training). Use this repo if you need the… See the full description on the dataset page: https://huggingface.co/datasets/jeremycochoy/wikimedia-pageview-timeseries-raw.us-layoffs-monthly-time-series-warn-act
US layoffs, month by month — 455 months of WARN notices, 1988-11 → 2026-09, rebuilt daily
Last rebuilt: 2026-09-25. One row per calendar month: how many US WARN Act layoff
notices were filed, how many workers they named, and how many states contributed —
as a regular series with every month present (zeros included), ready for pandas,
a chart or a forecasting model. A second table gives the same series per state.
455
consecutive months, 1988-11 → 2026-09, no gaps… See the full description on the dataset page: https://huggingface.co/datasets/APProjects/us-layoffs-monthly-time-series-warn-act.timeseries-1m-QQQ-5yHigh_Dimensional_Time_Series
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/Time-HD-Anonymous/High_Dimensional_Time_Series.timeseries-daily-sp500NeuraxonLife2.5-100K-TimeSeries
NeuraxonLife2.5-100K-DeepTimeSeries: Artificial Life Neuraxon Neural Network Simulation Deep Time Series Dataset
Dataset Description
The NeuraxonLife 2.5 Deep Time Series Dataset is a massive, comprehensive collection of simulation data from the Neuraxon Game of Life environment. It tracks the evolution of over 100,000 autonomous agents ("NxErs") evolving biologically-plausible neural networks under survival pressures.
This dataset represents a significant expansion over… See the full description on the dataset page: https://huggingface.co/datasets/DavidVivancos/NeuraxonLife2.5-100K-TimeSeries.timeseries-QQQ-1d-25yrTime-Series-Library
Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of… See the full description on the dataset page: https://huggingface.co/datasets/fluxae/Time-Series-Library.Time-Series-Library
Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current… See the full description on the dataset page: https://huggingface.co/datasets/Geenn2026/Time-Series-Library.faang-engineered-time-series-features-2013-2025
FAANG Stocks Historical Raw and Engineered Time-Series Dataset (2013-2025)
Since this is a comprehensive ReadMe file with multiple sections and crosslinks to other documents and images, I wanted to start by providing a ToC with hyperlinks to simplify navigation for the readers. (special thanks to @csavur for this very helpful suggestion!)
DOCUMENT NAVIGATION GUIDE (ToC)
1 - Summary2 - Usage & Reproducability3 - Practical Uses of this Dataset
3.1 - A real-world ML… See the full description on the dataset page: https://huggingface.co/datasets/ML-Owl/faang-engineered-time-series-features-2013-2025.TimeSeriesExam1
Dataset Card for TimeSeriesExam-1
This dataset provides Question-Answer (QA) pairs for the paper TimeSeriesExam: A Time Series Understanding Exam. Example inference code can be found here.
📖Introduction
Large Language Models (LLMs) have recently demonstrated a remarkable ability to model time series data. These capabilities can be partly explained if LLMs understand basic time series concepts. However, our knowledge of what these models understand about time series data… See the full description on the dataset page: https://huggingface.co/datasets/AutonLab/TimeSeriesExam1.Time-Series-Library
Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current… See the full description on the dataset page: https://huggingface.co/datasets/zxyang7/Time-Series-Library.timeseries-1m-QQQ-10yMultivariate_time_series_data_of_milling_processes_with_varying_tool_wear_and_machine_tools
Flattened version of the "Multivariate time series data of milling processes with varying tool wear and machine tools" dataset in the .parquet file format. Original dataset: https://doi.org/10.17632/zpxs87bjt8.3 and original paper: https://doi.org/10.1016/j.dib.2023.109574
Description
The presented dataset provides labeled, multivariate time series data of milling processes with varying tool wear and for varying machine tools. The width of the flank wear land VB… See the full description on the dataset page: https://huggingface.co/datasets/alpha-by-beta/Multivariate_time_series_data_of_milling_processes_with_varying_tool_wear_and_machine_tools.Time-Series-Library
Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current… See the full description on the dataset page: https://huggingface.co/datasets/lalababa/Time-Series-Library.timeseries_trending_youtube_videos_2019-04-15_to_2020-04-15Timeseries Trending YouTube Videos: 2019-04-15 to 2020-04-15
This dataset is a csv of one of the archived historical database tables queried from my non public database that contains time series data for period of 2019-04-15 to 2020-04-15. Video data was captured from the time they first appeared on trending list, and TSD exists until the video is removed from trending list.
This snapshot contains data for the 11,369 videos that appeared on trending within the timeframe, with 1,541,128 records… See the full description on the dataset page: https://huggingface.co/datasets/jettisonthenet/timeseries_trending_youtube_videos_2019-04-15_to_2020-04-15.Timeseries-QA
Timeseries-QA
This is a dataset for Timeseries Instruction Tuning.
It was created using the following steps:
Extracted features from time series data in AutonLab/Timeseries-PILE
microsoft/Phi-3-medium-4k-instruct generated the QA pairs
Timeseries Instruction Tuning用のデータセットです。
以下の手順で作成しました。
AutonLab/Timeseries-PILE の時系列データの特徴を抽出
microsoft/Phi-3-medium-4k-instruct がQAを作成
Dataset Details
Dataset Description
Curated by: HachiMLLanguage(s) (NLP): English… See the full description on the dataset page: https://huggingface.co/datasets/HachiML/Timeseries-QA.store-sales-time-series-forecasting
taken from this Kaggle competition:
Dataset Description
In this competition, you will predict sales for the thousands of product families sold at Favorita stores located in Ecuador. The training data includes dates, store and product information, whether that item was being promoted, as well as the sales numbers. Additional files include supplementary information that may be useful in building your models.
File Descriptions and Data Field Information… See the full description on the dataset page: https://huggingface.co/datasets/mrcksggcfc/store-sales-time-series-forecasting.time-series-benchmark
Four-Domain Real-World Forecasting Benchmark
Release r2026_09, schema 1.0, data version 2026-09-21.1.
This is a small evaluation corpus for general-purpose time-series forecasting models.
It contains real observations in four domains: electricity load, road traffic, river
discharge and near-surface weather. There are 13 datasets, 1,043 entities, 24.6M rows
and 43.0M measurement values, about 175 MB of Parquet in total.
Eleven datasets come straight from the original provider.… See the full description on the dataset page: https://huggingface.co/datasets/alinurdin/time-series-benchmark.Time-Series-Library
Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current… See the full description on the dataset page: https://huggingface.co/datasets/Leopegasus/Time-Series-Library.TimeSeriesDatatest
timeseries-1mn-sp500repro-time-series-saliency-maps-explaining-models-across-multiple-domains-traces
Agent traces
Agent sessions published from a Trackio Logbook.
timeseries-1mn-stocks
Dataset Card for "timeseries-1mn-stocks"
More Information needed
time_series_datasets
Tourism Monthly Time Series Dataset with Economic and Static Covariates
This dataset, originally sourced from Athanasopoulos et al. (2011), focuses on the tourism industry with a monthly frequency and has been enhanced with economic covariates (e.g., CPI, Inflation Rate, GDP) from official Australian government sources. We also perform some preprocessing to further increase the usability of the dataset with dynamic start dates for each series and static covariates for in-depth time… See the full description on the dataset page: https://huggingface.co/datasets/zaai-ai/time_series_datasets.