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Open Source Science

https://github.com/sky-uk/anticipy

A Python library for time series forecasting

https://github.com/sky-uk/anticipy

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 6 committers (16.7%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.6%) to scientific vocabulary

Keywords

forecasting python regression time-series
Last synced: 6 months ago · JSON representation

Repository

A Python library for time series forecasting

Basic Info
  • Host: GitHub
  • Owner: sky-uk
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Size: 350 KB
Statistics
  • Stars: 82
  • Watchers: 8
  • Forks: 14
  • Open Issues: 47
  • Releases: 0
Archived
Topics
forecasting python regression time-series
Created over 7 years ago · Last pushed 8 months ago
Metadata Files
Readme Contributing License

README.md

Latest Release Build Status Documentation Status Code Coverage pulls

Anticipy

Anticipy is a tool to generate forecasts for time series. It takes a pandas Series or DataFrame as input, and returns a DataFrame with the forecasted values for a given period of time.

Features:

  • Simple interface. Start forecasting with a single function call on a pandas DataFrame.
  • Model selection. If you provide different multiple models (e.g. linear, sigmoidal, exponential), the tool will compare them and choose the best fit for your data.
  • Trend and seasonality. Support for weekly and monthly seasonality, among other types.
  • Calendar events. Provide lists of special dates, such as holiday seasons or bank holidays, to improve model performance.
  • Data cleaning. The library has tools to identify and remove outliers, and to detect and handle step changes in the data.

It is straightforward to generate a simple linear model with the tool - just call forecast.runforecast(mydataframe):

```python import pandas as pd, numpy as np from anticipy import forecast

df = pd.DataFrame({'y': np.arange(0., 5)}, index=pd.daterange('2018-01-01', periods=5, freq='D')) dfforecast = forecast.runforecast(df, extrapolateyears=1) print(df_forecast.head(12)) ```

Output:

. date model y is_actuals 0 2018-01-01 y 0.000000e+00 True 1 2018-01-02 y 1.000000e+00 True 2 2018-01-03 y 2.000000e+00 True 3 2018-01-04 y 3.000000e+00 True 4 2018-01-05 y 4.000000e+00 True 5 2018-01-01 linear 5.551115e-17 False 6 2018-01-02 linear 1.000000e+00 False 7 2018-01-03 linear 2.000000e+00 False 8 2018-01-04 linear 3.000000e+00 False 9 2018-01-05 linear 4.000000e+00 False 10 2018-01-06 linear 5.000000e+00 False 11 2018-01-07 linear 6.000000e+00 False

Documentation is available in Read the Docs

Owner

  • Name: Sky UK Ltd
  • Login: sky-uk
  • Kind: organization
  • Location: United Kingdom

GitHub Events

Total
  • Issues event: 2
  • Push event: 1
  • Pull request event: 1
Last Year
  • Issues event: 2
  • Push event: 1
  • Pull request event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 113
  • Total Committers: 6
  • Avg Commits per committer: 18.833
  • Development Distribution Score (DDS): 0.133
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
capelastegui p****i@g****m 98
Nikolaos Bezirgiannidis n****s@g****m 6
Ioannis Begleris i****s@s****k 5
Sotiris Lenas s****s 2
Ioannis Begleris I****0@u****m 1
Bezirgiannidis, Nikolaos n****s@s****k 1
Committer Domains (Top 20 + Academic)
sky.uk: 1 ukc02x5fr6jg5h.bskyb.com: 1 soton.ac.uk: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 88
  • Total pull requests: 18
  • Average time to close issues: 4 months
  • Average time to close pull requests: 2 months
  • Total issue authors: 5
  • Total pull request authors: 4
  • Average comments per issue: 0.53
  • Average comments per pull request: 0.78
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • capelastegui (79)
  • bezes (4)
  • SkyDevSecOps-SCMA (3)
  • GREENENVIRON123 (2)
  • Ullah01 (1)
  • tarunkhanna1112 (1)
Pull Request Authors
  • capelastegui (15)
  • PaulCrossan (2)
  • ibegleris (1)
  • bezes (1)
Top Labels
Issue Labels
enhancement (16) bug (15) plot (5) good first issue (3)
Pull Request Labels
bug (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 281 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 13
  • Total maintainers: 2
pypi.org: anticipy

Forecasting tools

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 281 Last month
Rankings
Stargazers count: 7.8%
Downloads: 9.7%
Average: 9.9%
Dependent packages count: 10.1%
Forks count: 10.2%
Dependent repos count: 11.6%
Maintainers (2)
anticipy capelastegui
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • asv *
  • codecov *
  • numpy >=1.15.1
  • pandas >=0.23.0
  • plotly >=3.5.0
  • pycodestyle *
  • scipy >=1.0.0
  • sphinx >=2.1.2
  • sphinx-rtd-theme >=0.1.8
  • sphinxcontrib.serializinghtml >=1.1.3