Install & Compatibility
Where this runs
tested against v0.0.18 · 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
muslpy 3.10–3.95 runs
build_error
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 3.7s · import 0.096s · 87MB
88MB installed
● package 88MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
GroupedArray
✓ from coreforecast.grouped_array import GroupedArray
ExpandingMean
✓ from coreforecast.lag_transforms import ExpandingMean
LocalStandardScaler
✓ from coreforecast.scalers import LocalStandardScaler
This quickstart demonstrates the core usage of `coreforecast` by creating a `GroupedArray` and applying an `ExpandingMean` lag transformation and a `LocalStandardScaler`.
import numpy as np
from coreforecast.grouped_array import GroupedArray
from coreforecast.lag_transforms import ExpandingMean
from coreforecast.scalers import LocalStandardScaler
# The base data structure is the "grouped array"
# data: values of the series
# indptr: series boundaries such that data[indptr[i] : indptr[i + 1]] returns the i-th series.
# For example, if you have two series of sizes 3 and 7, indptr would be [0, 3, 10].
data = np.arange(10).astype(np.float32)
indptr = np.array([0, 3, 10], dtype=np.int32)
ga = GroupedArray(data, indptr)
# Apply transformations
exp_mean = ExpandingMean(lag=1).transform(ga)
scaler = LocalStandardScaler().fit(ga)
standardized = scaler.transform(ga)
print("Original data:", data)
print("GroupedArray indptr:", indptr)
print("Expanding Mean:", exp_mean)
print("Standardized data (first group):")
print(standardized.data[standardized.indptr[0]:standardized.indptr[1]])
Debug
Known issues
gotchacoreforecast is primarily a low-level dependency. Most users will interact with it indirectly through higher-level Nixtla libraries like MLForecast, StatsForecast, or NeuralForecast. Direct usage is for specific C++ operator needs.fixConsider if you truly need to use coreforecast directly, or if a higher-level library from Nixtlaverse better suits your use case.
affects: All versions
breakingVersion incompatibilities between coreforecast and other Nixtla libraries (e.g., mlforecast) can lead to 'AttributeError'. For instance, mlforecast==0.11.6 might not be compatible with older coreforecast versions.fixEnsure you are using compatible versions of coreforecast and any dependent Nixtla libraries. Check the documentation or GitHub issues of the higher-level library for recommended coreforecast versions. Upgrading all Nixtla libraries to their latest versions is generally recommended.
affects: Prior to 0.0.17 and specific mlforecast versions (e.g., mlforecast<0.12.0 with coreforecast<0.0.14)
gotchaThe `GroupedArray` data structure expects `data` and `indptr` to be 1D NumPy arrays. Incorrectly structuring `indptr` (e.g., not matching group boundaries) will lead to incorrect calculations or errors.fixCarefully construct the `indptr` array such that `data[indptr[i] : indptr[i + 1]]` correctly isolates each time series within the `data` array.
affects: All versions
Errors
Common errors & fixes
ModuleNotFoundError: No module named 'coreforecast'
The 'coreforecast' library is not installed in the current Python environment or the environment is not correctly activated.
fixInstall the library using pip: `pip install coreforecast` or with conda: `conda install -c conda-forge coreforecast`.
AttributeError: module 'coreforecast.lag_transforms' has no attribute 'BaseLagTransform'
This error typically occurs due to version incompatibility between `coreforecast` and `mlforecast` (or other Nixtla libraries that depend on it), where an older `coreforecast` version lacks the expected attribute.
fixUpgrade `coreforecast` to its latest version: `pip install -U coreforecast`, or ensure compatible versions of `coreforecast` and `mlforecast` are installed (e.g., `pip install mlforecast==0.11.7 coreforecast==0.0.3` for older setups, but generally upgrading both is recommended).
Kernel crashes on fit()
This issue is often related to conflicts with OpenMP dependencies, especially when `coreforecast` is used alongside other libraries like LightGBM or XGBoost in certain environments (e.g., Jupyter notebooks on macOS).
fixUpgrade `coreforecast` to version 0.0.11 or newer (version 0.0.17 is current and resolves this) as later versions removed the OpenMP dependency: `pip install -U coreforecast`. If the problem persists, try importing `scikit-learn` first, then `LightGBM`/`XGBoost`, and finally `mlforecast` (which uses `coreforecast` internally).
Upgrade
Version history
0.0.18latest on PyPI · released Jul 9, 2026
Audit
Dependencies
numpyrequiredRequired for the core data structures (GroupedArray) and numerical operations.