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Low similarity (12.4%) to scientific vocabulary
Repository
Time Series Forecasting with LightGBM
Basic Info
- Host: GitHub
- Owner: tblume1992
- License: mit
- Language: Python
- Default Branch: main
- Size: 200 KB
Statistics
- Stars: 85
- Watchers: 1
- Forks: 13
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
LazyProphet v0.3.8
Recent Changes
With v0.3.8 comes a fully fledged Optuna Optimizer for simple (no exogenous) regression problems. Classification is ToDo.
A Quick example of the new functionality:
``` from LazyProphet import LazyProphet as lp from sklearn.datasets import fetch_openml import matplotlib.pyplot as plt
bikesharing = fetchopenml("BikeSharingDemand", version=2, asframe=True) y = bikesharing.frame['count'] y = y[-400:].values
lpmodel = lp.LazyProphet.Optimize(y, seasonalperiod=[24, 168], nfolds=2, # must be greater than 1 ntrials=20, # number of optimization runs, default is 100 testsize=48 # size of the holdout set to test against ) fitted = lpmodel.fit(y) predicted = lp_model.predict(100)
plt.plot(y) plt.plot(np.append(fitted, predicted)) plt.axvline(400) plt.show() ```
Introduction
A decent intro can be found here.
LazyProphet is a time series forecasting model built for LightGBM forecasting of single time series.
Many nice-ities have been added such as recursive forecasting when using lagged target variable such as the last 4 values to predict the 5th.
Additionally, fourier basis functions and penalized weighted piecewise linear basis functions are options as well!
Don't ever use in-sample fit for these types of models as they fit the data quite snuggly.
Quickstart
pip install LazyProphet
Simple example from Sklearn, just give it the hyperparameters and an array:
``` from LazyProphet import LazyProphet as lp from sklearn.datasets import fetch_openml import matplotlib.pyplot as plt
bikesharing = fetchopenml("BikeSharingDemand", version=2, asframe=True) y = bikesharing.frame['count'] y = y[-400:].values
lpmodel = lp.LazyProphet(seasonalperiod=[24, 168], #list means we use both seasonal periods nbasis=4, #weighted piecewise basis functions fourierorder=10, ar=list(range(1,25)), decay=.99 #the 'penalized' in penalized weighted piecewise linear basis functions ) fitted = lpmodel.fit(y) predicted = lpmodel.predict(100)
plt.plot(y)
plt.plot(np.append(fitted, predicted))
plt.axvline(400)
plt.show()
```
If you are working with less data or then you will probably want to pass custom LightGBM params via boosting_params when creating the LazyProphet obj.
The default params are:
boosting_params = {
"objective": "regression",
"metric": "rmse",
"verbosity": -1,
"boosting_type": "gbdt",
"seed": 42,
'linear_tree': False,
'learning_rate': .15,
'min_child_samples': 5,
'num_leaves': 31,
'num_iterations': 50
}
WARNING
Passing lineartree=True can be extremely unstable, especially with ar and nbasis arguments. We do tests for linearity and will de-trend if necessary.
**
Most importantly for controlling the complexity by using numleaves/learningrate for complexity with less data.
Alternatively, you could try out the method:
tree_optimize(y, exogenous=None, cv_splits=3, test_size=None)
In-place of the fit method. This will do 'cv_splits' number of Time-Series Cross-Validation steps to optimize the tree using Optuna. This method has some degraded performance in testing but may be better for autoforecasting various types of data sizes.
Owner
- Name: Tyler Blume
- Login: tblume1992
- Kind: user
- Company: Newell Brands
- Website: https://www.linkedin.com/in/tyler-blume/
- Repositories: 6
- Profile: https://github.com/tblume1992
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: LazyProphet
message: >-
If you want to cite the work, please use this
information.
type: software
authors:
- given-names: Tyler
family-names: Blume
email: tyler.blume@mail.usf.edu
GitHub Events
Total
- Watch event: 6
- Fork event: 1
Last Year
- Watch event: 6
- Fork event: 1
Committers
Last synced: about 3 years ago
All Time
- Total Commits: 28
- Total Committers: 1
- Avg Commits per committer: 28.0
- Development Distribution Score (DDS): 0.0
Top Committers
| Name | Commits | |
|---|---|---|
| Tyler Blume | t****e@h****m | 28 |
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 3
- Total pull requests: 0
- Average time to close issues: 7 days
- Average time to close pull requests: N/A
- Total issue authors: 3
- Total pull request authors: 0
- Average comments per issue: 2.33
- 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
Top Authors
Issue Authors
- yuanjie-ai (1)
- robertmcleod2 (1)
- Cyue0316 (1)
Pull Request Authors
Top Labels
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Packages
- Total packages: 1
-
Total downloads:
- pypi 193 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 11
- Total maintainers: 1
pypi.org: lazyprophet
Time series forecasting with LightGBM
- Homepage: https://github.com/tblume1992/LazyProphet
- Documentation: https://lazyprophet.readthedocs.io/
- License: MIT License
-
Latest release: 0.3.9
published over 2 years ago
Rankings
Maintainers (1)
Dependencies
- lightgbm *
- matplotlib *
- numpy *
- optuna *
- pandas *
- scikit-learn *
- scipy *
- statsmodels *
- lightgbm *
- matplotlib *
- numpy *
- optuna *
- pandas *
- scikit-learn *
- scipy *
- statsmodels *