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Version bump
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@ -1,7 +1,7 @@
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Package: prophet
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Title: Automatic Forecasting Procedure
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Version: 0.1.1
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Date: 2017-04-17
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Version: 0.2
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Date: 2017-09-02
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Authors@R: c(
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person("Sean", "Taylor", email = "sjt@fb.com", role = c("cre", "aut")),
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person("Ben", "Letham", email = "bletham@fb.com", role = "aut")
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11
README.md
11
README.md
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@ -14,6 +14,7 @@ Prophet is [open source software](https://code.facebook.com/projects/) released
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- Prophet R package: https://cran.r-project.org/package=prophet
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- Prophet Python package: https://pypi.python.org/pypi/fbprophet/
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- Release blogpost: https://research.fb.com/prophet-forecasting-at-scale/
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- Prophet paper, "Forecasting at Scale": https://peerj.com/preprints/3190.pdf
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## Installation in R
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@ -59,6 +60,16 @@ Use `conda install gcc` to set up gcc. The easiest way to install Prophet is thr
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## Changelog
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### Version 0.2 (2017.09.02)
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- Forecasting with sub-daily data
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- Daily seasonality, and custom seasonalities
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- Extra regressors
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- Access to posterior predictive samples
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- Cross-validation function
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- Saturating minimums
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- Bugfixes
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### Version 0.1.1 (2017.04.17)
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- Bugfixes
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@ -7,4 +7,4 @@
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from fbprophet.forecaster import Prophet
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__version__ = '0.1.1'
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__version__ = '0.2'
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@ -532,7 +532,7 @@ class TestProphet(TestCase):
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fcst = m.predict(future)
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self.assertEqual(fcst.shape[1], 31)
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self.assertEqual(fcst['binary_feature'][0], 0)
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self.assertEqual(
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self.assertAlmostEqual(
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fcst['extra_regressors'][0],
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fcst['numeric_feature'][0] + fcst['binary_feature2'][0],
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)
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@ -97,7 +97,7 @@ class TestCommand(test_command):
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setup(
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name='fbprophet',
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version='0.1.1',
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version='0.2',
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description='Automatic Forecasting Procedure',
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url='https://facebookincubator.github.io/prophet/',
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author='Sean J. Taylor <sjt@fb.com>, Ben Letham <bletham@fb.com>',
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