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https://github.com/intive-DataScience/tbats
BATS and TBATS forecasting methods
https://github.com/intive-DataScience/tbats
Last synced: 3 months ago
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BATS and TBATS forecasting methods
- Host: GitHub
- URL: https://github.com/intive-DataScience/tbats
- Owner: intive-DataScience
- License: mit
- Created: 2018-12-21T14:19:21.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-04-17T10:22:27.000Z (almost 2 years ago)
- Last Synced: 2024-09-19T14:13:24.799Z (5 months ago)
- Language: Python
- Size: 104 KB
- Stars: 178
- Watchers: 7
- Forks: 19
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Authors: AUTHORS.txt
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README
# BATS and TBATS time series forecasting
Package provides BATS and TBATS time series forecasting methods described in:
> De Livera, A.M., Hyndman, R.J., & Snyder, R. D. (2011), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513-1527.
## Installation
From pypi:
```bash
pip install tbats
```
Import via:
```python
from tbats import BATS, TBATS
```
## Minimal working example:
```python
from tbats import TBATS
import numpy as np
# required on windows for multi-processing,
# see https://docs.python.org/2/library/multiprocessing.html#windows
if __name__ == '__main__':
np.random.seed(2342)
t = np.array(range(0, 160))
y = 5 * np.sin(t * 2 * np.pi / 7) + 2 * np.cos(t * 2 * np.pi / 30.5) + \
((t / 20) ** 1.5 + np.random.normal(size=160) * t / 50) + 10
# Create estimator
estimator = TBATS(seasonal_periods=[14, 30.5])
# Fit model
fitted_model = estimator.fit(y)
# Forecast 14 steps ahead
y_forecasted = fitted_model.forecast(steps=14)
# Summarize fitted model
print(fitted_model.summary())
```
Reading model details
```python
# Time series analysis
print(fitted_model.y_hat) # in sample prediction
print(fitted_model.resid) # in sample residuals
print(fitted_model.aic)
# Reading model parameters
print(fitted_model.params.alpha)
print(fitted_model.params.beta)
print(fitted_model.params.x0)
print(fitted_model.params.components.use_box_cox)
print(fitted_model.params.components.seasonal_harmonics)
```
See **examples** directory for more details.
## Troubleshooting
BATS and TBATS tries multitude of models under the hood and **may appear slow when fitting** to long time series. In order to speed it up you can start with constrained model search space. It is recommended to run it without Box-Cox transformation and ARMA errors modelling that are the slowest model elements:
```python
# Create estimator
estimator = TBATS(
seasonal_periods=[14, 30.5],
use_arma_errors=False, # shall try only models without ARMA
use_box_cox=False # will not use Box-Cox
)
fitted_model = estimator.fit(y)
```
In some environment configurations parallel computation of models freezes. Reason for this is unclear yet. If **the process appears to be stuck** you can try running it on a single core:
```python
estimator = TBATS(
seasonal_periods=[14, 30.5],
n_jobs=1
)
fitted_model = estimator.fit(y)
```
## For Contributors
Building package:
```bash
pip install -e .[dev]
```
Unit and integration tests:
```bash
pytest test/
```
R forecast package comparison tests. Those DO NOT RUN with default test command, you need R and forecast package installed:
```bash
pytest test_R/
```
## Comparison to R implementation
Python implementation is meant to be as much as possible equivalent to R implementation in forecast package.
- BATS in R https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/bats
- TBATS in R: https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/tbats