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https://github.com/cerlymarco/tspiral

A python package for time series forecasting with scikit-learn estimators.

https://github.com/cerlymarco/tspiral

Science Score: 13.0%

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    Low similarity (10.7%) to scientific vocabulary

Keywords

autoregressive-forecasters autoregressive-modeling direct-forecasting exogenous-predictors forecasting multivariate-forecasting multivariate-timeseries python recursive-forecasting scikit-learn time-series timeseries timeseries-forecasting
Last synced: 6 months ago · JSON representation

Repository

A python package for time series forecasting with scikit-learn estimators.

Basic Info
  • Host: GitHub
  • Owner: cerlymarco
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 2.73 MB
Statistics
  • Stars: 161
  • Watchers: 5
  • Forks: 19
  • Open Issues: 0
  • Releases: 2
Topics
autoregressive-forecasters autoregressive-modeling direct-forecasting exogenous-predictors forecasting multivariate-forecasting multivariate-timeseries python recursive-forecasting scikit-learn time-series timeseries timeseries-forecasting
Created over 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

README.md

tspiral

A python package for time series forecasting with scikit-learn estimators.

tspiral is not a library that works as a wrapper for other tools and methods for time series forecasting. tspiral directly provides scikit-learn estimators for time series forecasting. It leverages the benefit of using scikit-learn syntax and components to easily access the open source ecosystem built on top of the scikit-learn community. It easily maps a complex time series forecasting problems into a tabular supervised regression task, solving it with a standard approach.

Overview

tspiral provides 4 optimized forecasting techniques:

  • Recursive Forecasting

Lagged target features are combined with exogenous regressors (if provided) and lagged exogenous features (if specified). A scikit-learn compatible regressor is fitted on the whole merged data. The fitted estimator is called iteratively to predict multiple steps ahead.

recursive-standard

Which in a compact way we can summarize in:

recursive-compact

  • Direct Forecasting

A scikit-learn compatible regressor is fitted on the lagged data for each time step to forecast.

direct

Which in a compact way we can summarize in:

direct-compact

It's also possible to mix recursive and direct forecasting by predicting directly some future horizons while using recursive on the remaining.

directmix-compact

  • Stacking Forecasting

Multiple recursive time series forecasters are fitted and combined on the final portion of the training data with a meta-learner.

stacked

  • Rectified Forecasting

Multiple direct time series forecasters are fitted and combined on the final portion of the training data with a meta-learner.

rectify

GLOBAL and MULTIVARIATE time series forecasting are natively supported for all the forecasting methods available. For GLOBAL forecasting, use the groups parameter to specify the column of the input data that contains the group identifiers. For MULTIVARIATE forecasting, pass a target with multiple columns when calling fit.

Installation

shell pip install --upgrade tspiral The module depends only on NumPy, Pandas, and Scikit-Learn (>=0.24.2). Python 3.6 or above is supported.

Media

Usage

  • Recursive Forecasting python import numpy as np from sklearn.linear_model import Ridge from tspiral.forecasting import ForecastingCascade timesteps = 400 e = np.random.normal(0,1, (timesteps,)) y = np.concatenate([ 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e, 2*np.cos(np.arange(timesteps)*(2*np.pi/24))+e, ]) X = [[0]]*timesteps+[[1]]*timesteps model = ForecastingCascade( Ridge(), lags=range(1,24+1), groups=[0], ).fit(X, y) forecasts = model.predict([[0]]*80+[[1]]*80)

  • Direct Forecasting python import numpy as np from sklearn.linear_model import Ridge from tspiral.forecasting import ForecastingChain timesteps = 400 e = np.random.normal(0,1, (timesteps,)) y = np.concatenate([ 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e, 2*np.cos(np.arange(timesteps)*(2*np.pi/24))+e, ]) X = [[0]]*timesteps+[[1]]*timesteps model = ForecastingChain( Ridge(), n_estimators=24, lags=range(1,24+1), groups=[0], ).fit(X, y) forecasts = model.predict([[0]]*80+[[1]]*80)

  • Stacking Forecasting python import numpy as np from sklearn.linear_model import Ridge from sklearn.tree import DecisionTreeRegressor from tspiral.forecasting import ForecastingStacked timesteps = 400 e = np.random.normal(0,1, (timesteps,)) y = np.concatenate([ 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e, 2*np.cos(np.arange(timesteps)*(2*np.pi/24))+e, ]) X = [[0]]*timesteps+[[1]]*timesteps model = ForecastingStacked( [Ridge(), DecisionTreeRegressor()], test_size=24*3, lags=range(1,24+1), groups=[0], ).fit(X, y) forecasts = model.predict([[0]]*80+[[1]]*80)

  • Rectified Forecasting python import numpy as np from sklearn.linear_model import Ridge from sklearn.tree import DecisionTreeRegressor from tspiral.forecasting import ForecastingRectified timesteps = 400 e = np.random.normal(0,1, (timesteps,)) y = np.concatenate([ 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e, 2*np.cos(np.arange(timesteps)*(2*np.pi/24))+e, ]) X = [[0]]*timesteps+[[1]]*timesteps model = ForecastingRectified( [Ridge(), DecisionTreeRegressor()], n_estimators=24*3, test_size=24*3, lags=range(1,24+1), groups=[0], ).fit(X, y) forecasts = model.predict([[0]]*80+[[1]]*80)

More examples in the notebooks folder.

Owner

  • Name: Marco Cerliani
  • Login: cerlymarco
  • Kind: user
  • Location: Milan, Italy

Statistician Hacker & Data Scientist

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 23
  • Total Committers: 2
  • Avg Commits per committer: 11.5
  • Development Distribution Score (DDS): 0.13
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
cerlymarco 3****o 20
marco.cerliani m****i@a****u 3
Committer Domains (Top 20 + Academic)
a2a.eu: 1

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 3
  • Total pull requests: 0
  • Average time to close issues: about 2 months
  • Average time to close pull requests: N/A
  • Total issue authors: 3
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • Sandy4321 (1)
  • jorisparet (1)
  • liuwenfeng93 (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 45 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 1
  • Total versions: 8
  • Total maintainers: 1
pypi.org: tspiral

A python package for time series forecasting with scikit-learn estimators.

  • Versions: 8
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 45 Last month
Rankings
Dependent packages count: 4.7%
Stargazers count: 6.3%
Forks count: 8.7%
Average: 11.3%
Downloads: 15.1%
Dependent repos count: 21.6%
Maintainers (1)
cerlymarco
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • numpy *
  • scikit-learn >=0.24.2
  • scipy *
setup.py pypi