{"author":"Nixtla","author_email":"business@nixtla.io","bugtrack_url":null,"classifiers":["Development Status :: 4 - Beta","Intended Audience :: Developers","License :: OSI Approved :: Apache Software License","Natural Language :: English","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.9"],"description":"utilsforecast\n================\n\n\n\n## Install\n\n### PyPI\n\n``` sh\npip install utilsforecast\n```\n\n### Conda\n\n``` sh\nconda install -c conda-forge utilsforecast\n```\n\n## How to use\n\n### Generate synthetic data\n\n``` python\nfrom utilsforecast.data import generate_series\n```\n\n``` python\nseries = generate_series(3, with_trend=True, static_as_categorical=False)\nseries\n```\n\n

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	unique_id	ds	y
0	0	2000-01-01	0.422133
1	0	2000-01-02	1.501407
2	0	2000-01-03	2.568495
3	0	2000-01-04	3.529085
4	0	2000-01-05	4.481929
...	...	...	...
481	2	2000-06-11	163.914625
482	2	2000-06-12	166.018479
483	2	2000-06-13	160.839176
484	2	2000-06-14	162.679603
485	2	2000-06-15	165.089288

486 rows × 3 columns

\n\n### Plotting\n\n``` python\nfrom utilsforecast.plotting import plot_series\n```\n\n``` python\nfig = plot_series(series, plot_random=False, max_insample_length=50, engine='matplotlib')\nfig.savefig('imgs/index.png', bbox_inches='tight')\n```\n\n![](https://raw.githubusercontent.com/Nixtla/utilsforecast/main/nbs/imgs/index.png)\n\n### Preprocessing\n\n``` python\nfrom utilsforecast.preprocessing import fill_gaps\n```\n\n``` python\nserie = series[series['unique_id'].eq(0)].tail(10)\n# drop some points\nwith_gaps = serie.sample(frac=0.5, random_state=0).sort_values('ds')\nwith_gaps\n```\n\n

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	ds	y
213	2000-08-01	18.543147
214	2000-08-02	19.941764
216	2000-08-04	21.968733
220	2000-08-08	19.091509
221	2000-08-09	20.220739

\n\n``` python\nfill_gaps(with_gaps, freq='D')\n```\n\n

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	ds	y
0	2000-08-01	18.543147
1	2000-08-02	19.941764
2	2000-08-03	NaN
3	2000-08-04	21.968733
4	2000-08-05	NaN
5	2000-08-06	NaN
6	2000-08-07	NaN
7	2000-08-08	19.091509
8	2000-08-09	20.220739

\n\n### Evaluating\n\n``` python\nfrom functools import partial\n\nimport numpy as np\n\nfrom utilsforecast.evaluation import evaluate\nfrom utilsforecast.losses import mape, mase\n```\n\n``` python\nvalid = series.groupby('unique_id').tail(7).copy()\ntrain = series.drop(valid.index)\nrng = np.random.RandomState(0)\nvalid['seas_naive'] = train.groupby('unique_id')['y'].tail(7).values\nvalid['rand_model'] = valid['y'] * rng.rand(valid['y'].shape[0])\ndaily_mase = partial(mase, seasonality=7)\nevaluate(valid, metrics=[mape, daily_mase], train_df=train)\n```\n\n

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

	unique_id	metric	seas_naive	rand_model
0	0	mape	0.024139	0.440173
1	1	mape	0.054259	0.278123
2	2	mape	0.042642	0.480316
3	0	mase	0.907149	16.418014
4	1	mase	0.991635	6.404254
5	2	mase	1.013596	11.365040

\n","description_content_type":"text/markdown","docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":["Author","Author-Email","Classifier","Description","Description-Content-Type","Home-Page","Keywords","License","Provides-Extra","Requires-Dist","Requires-Python","Summary"],"home_page":"https://github.com/Nixtla/utilsforecast","keywords":"time-series analysis forecasting","license":"Apache Software License 2.0","license_expression":null,"license_files":null,"maintainer":null,"maintainer_email":null,"name":"utilsforecast","package_url":"https://pypi.org/project/utilsforecast/","platform":null,"project_url":"https://pypi.org/project/utilsforecast/","project_urls":{"Homepage":"https://github.com/Nixtla/utilsforecast"},"provides_extra":["plotting","polars","dev"],"release_url":"https://pypi.org/project/utilsforecast/0.2.12/","requires_dist":["numpy","packaging","pandas>=1.1.1","pandas[plot]; extra == \"plotting\"","plotly; extra == \"plotting\"","plotly-resampler; extra == \"plotting\"","polars[numpy]; extra == \"polars\"","numba>=0.58.0; extra == \"dev\"","scipy; extra == \"dev\"","datasetsforecast==0.0.8; extra == \"dev\"","polars[numpy]; extra == \"dev\"","pandas[plot]; extra == \"dev\"","plotly; extra == \"dev\"","nbdev<2.3.26; extra == \"dev\"","plotly-resampler; extra == \"dev\"","black; extra == \"dev\"","pyarrow; extra == \"dev\""],"requires_python":">=3.9","summary":"Forecasting utilities","version":"0.2.12","yanked":false,"yanked_reason":null}

Dependencies of utilsforecast (12).
Dependency	Optional
numpy	false
packaging	false
pandas	false
black	true
datasetsforecast	true
nbdev	true
numba	true
plotly	true
plotly-resampler	true
polars	true
pyarrow	true
scipy	true

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