BATS and TBATS for time series forecasting
Project description
# 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
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
## For Contributors
Building package:
```bash
pip install -e .[dev]
```
Unit and integration tests:
```bash
python setup.py test
```
R forecast package comparison tests. Those DO NOT RUN with default test command, you need R forecast package installed:
```bash
python setup.py 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
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
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
## For Contributors
Building package:
```bash
pip install -e .[dev]
```
Unit and integration tests:
```bash
python setup.py test
```
R forecast package comparison tests. Those DO NOT RUN with default test command, you need R forecast package installed:
```bash
python setup.py 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
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