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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteUseful time-series datasets depend on the task: classification assigns a label to each sequence, forecasting predicts future values, and regression maps a sequence to a scalar. For classification benchmarks, start with UCR or UEA; for forecasting, consider the Monash repository and its M3, M4, Tourism, or NN5 collections. These seven options are not a universal ranking—their formats, domains, frequencies, and scales differ.
First, match the dataset to the machine-learning task
- Classification: Each time series is an example with a label, such as a class or category.
- Forecasting: A model uses past observations to predict future values. The forecast horizon and sampling frequency matter.
- Regression: A model maps a time series to a numeric target. Some time-series archives include regression collections as well as classification and clustering data.
File extensions can help identify the intended use, but check the dataset documentation rather than inferring every property from a filename. The aeon data-loading documentation describes .ts for classification, clustering, and regression collections, and .tsf for forecasting collections. It also documents ARFF, TSV, and CSV routes; supported formats do not, by themselves, grant reuse rights.
Seven time-series datasets to consider
1. UCR Time Series Classification Archive
UCR is a practical starting point for univariate time-series classification benchmarks. Its archive page provides a briefing document and a downloadable ZIP archive of about 260 MB (the page’s figure, accessed in 2026). The archive page advises: “We suggest you begin by reading the briefing document in PDF or PowerPoint, which also contains the password.” Follow its instructions before attempting to use the download.
The Monash archive paper described UCR as univariate and reported 128 datasets at the time of publication; that is a historical count, not a current inventory. Check the UCR archive page for current materials.
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2. UEA multivariate classification archive
UEA is a useful counterpart to UCR when each example has multiple channels or dimensions. The Monash paper described 30 multivariate datasets in the UEA archive at publication time. Treat that as a dated figure: inspect each dataset’s current metadata to establish its number of channels, sequence lengths, and missing-value characteristics rather than assuming they are uniform.
See the aeon documentation for data-loading routes, and consult the archive’s underlying dataset sources for their terms.
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3. Monash Time Series Forecasting Repository
For forecasting across collections of related series, the Monash repository is a broad entry point. Its live page describes 30 datasets and 58 variations, with public and curated real-world and competition data, and provides R and Python loading wrappers. The page was updated through November 2025; the inventory may change. The repository says its data are intended for research use, so check applicable terms before other uses.
This live inventory differs from the original archive paper’s description of 20 public datasets and six very long single series, published in 2021. These figures describe different publication-era snapshots, not a contradiction to resolve by treating either as a timeless total. Explore the Monash Time Series Forecasting Repository.
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4. M3 competition dataset
M3 suits forecasting work that needs several frequencies and domains. The 2021 Monash archive paper describes 3,003 series at yearly, quarterly, and monthly frequencies across six domains. Those are paper-era characteristics, not a current package inventory. Confirm the dataset version and forecasting setup you use.
5. M4 competition dataset
M4 offers a much larger and more frequency-diverse forecasting collection. The 2021 paper describes 100,000 series spanning yearly, quarterly, monthly, weekly, daily, and hourly frequencies. That scale can be useful for broad evaluation, but it is not a requirement for every experiment; choose a dataset sized and structured for the question you are testing.
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6. Tourism forecasting dataset
The Tourism collection provides a domain-specific forecasting option. The 2021 paper describes 1,311 tourism-related series at yearly, quarterly, and monthly frequencies. It can be a sensible choice when tourism is a domain you can interpret and want your evaluation to reflect.
7. NN5 dataset
NN5 contains 111 daily UK ATM cash-withdrawal series, with a competition forecast horizon of 56 steps, according to the 2021 Monash paper. The paper describes original data with missing values and a median-imputed variant. Record which version and preprocessing you use; results from the two should not be treated as directly interchangeable.
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The Monash paper also describes Wikipedia Web Traffic as an alternative for a much larger daily collection: 145,063 page-hit series from 2015-07-01 to 2017-09-10, with original and imputed versions. That historical description does not establish current availability or terms. Consider it when its scale and page-traffic domain suit the question, and verify the current source before use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose among the seven
- Task: Decide whether you need labeled classification examples, forecasting targets, or a series-to-scalar regression problem. Do not compare results across different task types as if they measured the same capability.
- Channels and lengths: Establish whether data are univariate or multivariate, and whether sequences have equal or variable lengths. Check each dataset’s metadata; archive-level labels do not guarantee identical structure for every collection.
- Scale and domain: Match the amount of data to your compute and evaluation needs, and ask whether the application domain is relevant. Historical descriptions range from NN5’s 111 series to Wikipedia Web Traffic’s 145,063 series; neither size is inherently better.
- Frequency and horizon: Confirm that sampling frequency and prediction horizon match your intended use. A daily dataset with a 56-step competition horizon answers a different forecasting question from an hourly or yearly collection.
- Missingness and preprocessing: Determine whether the selected release is raw or imputed and record any preprocessing. A model comparison can be misleading if competitors receive differently processed versions.
- Access and terms: Check the current source page, data format, loading tools, and terms for the underlying dataset. Terms may vary among datasets within an archive; format availability is not evidence of permission for commercial or sensitive use.
Evaluate models on comparable terms
A single score cannot fairly rank these datasets across different tasks, frequencies, horizons, and scales. Align the evaluation metric and forecasting setup with the question, and state the dataset version and preprocessing used.
The Monash repository reports using MASE for evaluation. Its evaluation guidance notes that MAE and RMSE are suitable for broad comparison only when series share units, and characterizes sMAPE as mostly useful for legacy competition settings. See the Monash repository for its evaluation context. Avoid presenting one metric as a universal leaderboard across all seven collections.
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