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@@ -0,0 +1 @@ +/tbats-1.1.2.tar.gz diff --git a/python-tbats.spec b/python-tbats.spec new file mode 100644 index 0000000..ad3f679 --- /dev/null +++ b/python-tbats.spec @@ -0,0 +1,442 @@ +%global _empty_manifest_terminate_build 0 +Name: python-tbats +Version: 1.1.2 +Release: 1 +Summary: BATS and TBATS for time series forecasting +License: MIT License +URL: https://github.com/intive-DataScience/tbats +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d2/3f/51a5272d9672e1d4c95630da0077eef709a82b7956345223fd2df8206b25/tbats-1.1.2.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-pmdarima +Requires: python3-scikit-learn +Requires: python3-pip-tools +Requires: python3-pytest +Requires: python3-rpy2 + +%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 + +# 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 + + + + + + + + +%package -n python3-tbats +Summary: BATS and TBATS for time series forecasting +Provides: python-tbats +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-tbats +# 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 + + + + + + + + +%package help +Summary: Development documents and examples for tbats +Provides: python3-tbats-doc +%description help +# 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 + + + + + + + + +%prep +%autosetup -n tbats-1.1.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-tbats -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.2-1 +- Package Spec generated @@ -0,0 +1 @@ +9dfb85fe788728aad738b4c4f5044d1a tbats-1.1.2.tar.gz |