automatic import of python-tbats

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+/tbats-1.1.2.tar.gz
diff --git a/python-tbats.spec b/python-tbats.spec
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+%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
diff --git a/sources b/sources
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@@ -0,0 +1 @@
+9dfb85fe788728aad738b4c4f5044d1a  tbats-1.1.2.tar.gz