Registry / data / datasetsforecast

datasetsforecast

library1.0.1pypypi✓ verified 85d ago

A Python library providing popular time series forecasting datasets (M3, M4, M5, etc.) with easy loading, splitting, and preprocessing. Current version 1.0.1, released June 2025. Maintained by Nixtla, with monthly releases.

data

pip install datasetsforecast

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · DATASETSFORECAST

datasetsforecast

datapythonv1.0.1

Install

20.6s avg

Import

1832ms

Disk

449MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.0.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 20.6s · import 1.832s · 445MB

449MB installed

● package 449MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

✓ from datasetsforecast.m4 import M4

✗ from datasetsforecast import M4

M4 is a class in the m4 module, not top-level.

✓ from datasetsforecast.m5 import M5

✗ from datasetsforecast import M5

M5 is a class in the m5 module.

HierarchicalData

✓ from datasetsforecast.hierarchical import HierarchicalData

✗ from datasetsforecast import HierarchicalData

HierarchicalData is in the hierarchical module.

Load the M4 yearly dataset and preview the training set.

from datasetsforecast.m4 import M4 dataset = M4.load('Yearly') print(dataset['train'].head())

Debug

Known issues

breakingIn v1.0.0, the `losses` and `evaluation` modules were removed. Any imports from `datasetsforecast.losses` or `datasetsforecast.evaluation` will fail.

fix

Remove imports of `losses` and `evaluation`. Use alternative libraries like `numpy` or `scikit-learn` for metrics.

affects: >=1.0.0

gotchaDataset classes (M3, M4, M5, etc.) are not directly importable from the top-level `datasetsforecast` package. You must import from the submodule (e.g., `from datasetsforecast.m4 import M4`).

fix

Use correct submodule path as shown in the imports section.

affects: all

gotchaThe `M3` dataset download URL changed in v1.0.1. If you're on an older version, you may get a 404 error. Upgrade to >=1.0.1.

fix

Run `pip install datasetsforecast>=1.0.1`.

affects: <1.0.1

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'datasetsforecast.losses'

The 'losses' module was removed in v1.0.0 as a breaking change.

fix

Remove any import of `datasetsforecast.losses`. Use another metrics library or compute metrics manually.

AttributeError: module 'datasetsforecast' has no attribute 'M4'

M4 is not exported at the top level; it's in `datasetsforecast.m4`.

fix

Change the import to `from datasetsforecast.m4 import M4`.

requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://zenodo.org/record/...

Outdated dataset download URL, fixed in v1.0.1 for M3 and other datasets.

fix

Upgrade to datasetsforecast>=1.0.1: `pip install --upgrade datasetsforecast`.

Upgrade

Version history

1.0.1latest on PyPI · released Feb 24, 2026

Audit

Dependencies

pandasrequiredData manipulation

numpyrequiredNumerical arrays

requestsrequiredData download

Agent activity

15 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

githubhttps://github.com/Nixtla/datasetsforecast ↗

packagehttps://pypi.org/project/datasetsforecast/ ↗

homepagehttps://nixtla.github.io/datasetsforecast ↗

docshttps://nixtlaverse.nixtla.io/datasetsforecast/ ↗