diff --git a/notebooks/saturating_forecasts.ipynb b/notebooks/saturating_forecasts.ipynb index 9f0056e..ecb4be7 100644 --- a/notebooks/saturating_forecasts.ipynb +++ b/notebooks/saturating_forecasts.ipynb @@ -4,10 +4,19 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "block_hidden": true, - "collapsed": true + "block_hidden": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\lydia\\documents\\ryan_work\\prophet\\python\\fbprophet\\diagnostics.py:10: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", + " from tqdm.autonotebook import tqdm\n", + "Importing plotly failed. Interactive plots will not work.\n" + ] + } + ], "source": [ "%load_ext rpy2.ipython\n", "%matplotlib inline\n", @@ -288,25 +297,64 @@ "source": [ "To use a logistic growth trend with a saturating minimum, a maximum capacity must also be specified." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Constant Trend" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For some datasets which exhibit strong seasonality patterns rather than trend changes, it may be useful to force the growth rate to be flat. This can be achieved simply by passing `growth=flat` before fitting the model. However, if used on a dataset with obvious non linear trend, most of the change in the trend would be fit with variance (high noise term) and hence we would observe a high predictive uncertainty." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.read_csv('../examples/example_retail_sales.csv')\n", + "m = Prophet(growth='flat')\n", + "m.fit(df)\n", + "future = m.make_future_dataframe(periods=3652)\n", + "fcst = m.predict(future)\n", + "fig = m.plot(fcst)" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python (prophet)", "language": "python", - "name": "python2" + "name": "prophet" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14+" + "pygments_lexer": "ipython3", + "version": "3.8.2" } }, "nbformat": 4, diff --git a/python/fbprophet/forecaster.py b/python/fbprophet/forecaster.py index d03d876..e90835a 100644 --- a/python/fbprophet/forecaster.py +++ b/python/fbprophet/forecaster.py @@ -155,9 +155,9 @@ class Prophet(object): def validate_inputs(self): """Validates the inputs to Prophet.""" - if self.growth not in ('linear', 'logistic'): + if self.growth not in ('linear', 'logistic', 'flat'): raise ValueError( - 'Parameter "growth" should be "linear" or "logistic".') + 'Parameter "growth" should be "linear", "logistic" or "flat".') if ((self.changepoint_range < 0) or (self.changepoint_range > 1)): raise ValueError('Parameter "changepoint_range" must be in [0, 1]') if self.holidays is not None: @@ -1049,6 +1049,27 @@ class Prophet(object): k = (L0 - L1) / T return (k, m) + @staticmethod + def flat_growth_init(df): + """Initialize flat growth. + + Provides a strong initialization for flat growth. Sets the growth to 0 + and offset parameter as mean of history y_scaled values. + + Parameters + ---------- + df: pd.DataFrame with columns ds (date), y_scaled (scaled time series), + and t (scaled time). + + Returns + ------- + A tuple (k, m) with the rate (k) and offset (m) of the linear growth + function. + """ + k = 0 + m = df['y_scaled'].mean() + return k, m + def fit(self, df, **kwargs): """Fit the Prophet model. @@ -1098,6 +1119,8 @@ class Prophet(object): self.set_changepoints() + trend_indicator = {'linear': 0, 'logistic': 1, 'flat': 2} + dat = { 'T': history.shape[0], 'K': seasonal_features.shape[1], @@ -1108,7 +1131,7 @@ class Prophet(object): 'X': seasonal_features, 'sigmas': prior_scales, 'tau': self.changepoint_prior_scale, - 'trend_indicator': int(self.growth == 'logistic'), + 'trend_indicator': trend_indicator[self.growth], 's_a': component_cols['additive_terms'], 's_m': component_cols['multiplicative_terms'], } @@ -1116,6 +1139,9 @@ class Prophet(object): if self.growth == 'linear': dat['cap'] = np.zeros(self.history.shape[0]) kinit = self.linear_growth_init(history) + elif self.growth == 'flat': + dat['cap'] = np.zeros(self.history.shape[0]) + kinit = self.flat_growth_init(history) else: dat['cap'] = history['cap_scaled'] kinit = self.logistic_growth_init(history) @@ -1128,9 +1154,8 @@ class Prophet(object): 'sigma_obs': 1, } - if (history['y'].min() == history['y'].max() - and self.growth == 'linear'): - # Nothing to fit. + if history['y'].min() == history['y'].max() and \ + (self.growth == 'linear' or self.growth == 'flat'): self.params = stan_init self.params['sigma_obs'] = 1e-9 for par in self.params: @@ -1255,6 +1280,22 @@ class Prophet(object): m_t[indx] += gammas[s] return cap / (1 + np.exp(-k_t * (t - m_t))) + @staticmethod + def flat_trend(t, m): + """Evaluate the flat trend function. + + Parameters + ---------- + t: np.array of times on which the function is evaluated. + m: Float initial offset. + + Returns + ------- + Vector y(t). + """ + m_t = m * np.ones_like(t) + return m_t + def predict_trend(self, df): """Predict trend using the prophet model. @@ -1273,10 +1314,13 @@ class Prophet(object): t = np.array(df['t']) if self.growth == 'linear': trend = self.piecewise_linear(t, deltas, k, m, self.changepoints_t) - else: + elif self.growth == 'logistic': cap = df['cap_scaled'] trend = self.piecewise_logistic( t, cap, deltas, k, m, self.changepoints_t) + elif self.growth == 'flat': + # constant trend + trend = self.flat_trend(t, m) return trend * self.y_scale + df['floor'] @@ -1470,10 +1514,12 @@ class Prophet(object): if self.growth == 'linear': trend = self.piecewise_linear(t, deltas, k, m, changepoint_ts) - else: + elif self.growth == 'logistic': cap = df['cap_scaled'] trend = self.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts) + elif self.growth == 'flat': + trend = self.flat_trend(t, m) return trend * self.y_scale + df['floor'] diff --git a/python/fbprophet/tests/test_diagnostics.py b/python/fbprophet/tests/test_diagnostics.py index 3796303..cacf202 100644 --- a/python/fbprophet/tests/test_diagnostics.py +++ b/python/fbprophet/tests/test_diagnostics.py @@ -106,18 +106,21 @@ class TestDiagnostics(TestCase): self.assertEqual(diagnostics.single_cutoff_forecast.call_count, forecasts) - - def test_cross_validation_logistic(self): - df = self.__df.copy() - df['cap'] = 40 - m = Prophet(growth='logistic').fit(df) - df_cv = diagnostics.cross_validation( - m, horizon='1 days', period='1 days', initial='140 days') - self.assertEqual(len(np.unique(df_cv['cutoff'])), 2) - self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all()) - df_merged = pd.merge(df_cv, self.__df, 'left', on='ds') - self.assertAlmostEqual( - np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0) + def test_cross_validation_logistic_or_flat_growth(self): + params = (x for x in ['logistic', 'flat']) + for growth in params: + with self.subTest(i=growth): + df = self.__df.copy() + if growth == "logistic": + df['cap'] = 40 + m = Prophet(growth=growth).fit(df) + df_cv = diagnostics.cross_validation( + m, horizon='1 days', period='1 days', initial='140 days') + self.assertEqual(len(np.unique(df_cv['cutoff'])), 2) + self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all()) + df_merged = pd.merge(df_cv, self.__df, 'left', on='ds') + self.assertAlmostEqual( + np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0) def test_cross_validation_extra_regressors(self): df = self.__df.copy() diff --git a/python/fbprophet/tests/test_prophet.py b/python/fbprophet/tests/test_prophet.py index 994d755..1682792 100644 --- a/python/fbprophet/tests/test_prophet.py +++ b/python/fbprophet/tests/test_prophet.py @@ -206,22 +206,41 @@ class TestProphet(TestCase): # Check for approximate shift invariance self.assertTrue((np.abs(fcst1['yhat'] - fcst2['yhat']) < 1).all()) - def test_get_changepoints(self): - m = Prophet() + def test_flat_growth(self): + m = Prophet(growth='flat') N = DATA.shape[0] history = DATA.head(N // 2).copy() + m.fit(history) + future = m.make_future_dataframe(N // 2, include_history=False) + fcst = m.predict(future) + m_ = m.params['m'] + k = m.params['k'] + self.assertEqual(k[0, 0], 0) + self.assertEqual(fcst['trend'].unique(), m_*m.y_scale) + self.assertEqual(np.round(m_[0,0]*m.y_scale), 26) - history = m.setup_dataframe(history, initialize_scales=True) - m.history = history + def test_invalid_growth_input(self): + msg = 'Parameter "growth" should be "linear", ' \ + '"logistic" or "flat".' + with self.assertRaisesRegex(ValueError, msg): + Prophet(growth="constant") - m.set_changepoints() + def test_get_changepoints(self): + m = Prophet() + N = DATA.shape[0] + history = DATA.head(N // 2).copy() - cp = m.changepoints_t - self.assertEqual(cp.shape[0], m.n_changepoints) - self.assertEqual(len(cp.shape), 1) - self.assertTrue(cp.min() > 0) - cp_indx = int(np.ceil(0.8 * history.shape[0])) - self.assertTrue(cp.max() <= history['t'].values[cp_indx]) + history = m.setup_dataframe(history, initialize_scales=True) + m.history = history + + m.set_changepoints() + + cp = m.changepoints_t + self.assertEqual(cp.shape[0], m.n_changepoints) + self.assertEqual(len(cp.shape), 1) + self.assertTrue(cp.min() > 0) + cp_indx = int(np.ceil(0.8 * history.shape[0])) + self.assertTrue(cp.max() <= history['t'].values[cp_indx]) def test_set_changepoint_range(self): m = Prophet(changepoint_range=0.4) @@ -301,6 +320,10 @@ class TestProphet(TestCase): self.assertAlmostEqual(k, 1.507925, places=4) self.assertAlmostEqual(m, -0.08167497, places=4) + k,m = model.flat_growth_init(history) + self.assertEqual(k, 0) + self.assertAlmostEqual(m, 0.49335657, places=4) + def test_piecewise_linear(self): model = Prophet() @@ -342,6 +365,18 @@ class TestProphet(TestCase): y = model.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts) self.assertAlmostEqual((y - y_true).sum(), 0.0, places=5) + def test_flat_trend(self): + model = Prophet() + t = np.arange(11) + m = 0.5 + y = model.flat_trend(t, m) + y_true = np.array([0.5]*11) + self.assertEqual((y - y_true).sum(), 0) + t = t[8:] + y_true = y_true[8:] + y = model.flat_trend(t, m) + self.assertEqual((y - y_true).sum(), 0) + def test_holidays(self): holidays = pd.DataFrame({ 'ds': pd.to_datetime(['2016-12-25']), diff --git a/python/stan/unix/prophet.stan b/python/stan/unix/prophet.stan index 79db33c..cb18633 100644 --- a/python/stan/unix/prophet.stan +++ b/python/stan/unix/prophet.stan @@ -73,6 +73,15 @@ functions { ) { return (k + A * delta) .* t + (m + A * (-t_change .* delta)); } + + // Flat trend function + + vector flat_trend( + real m, + int T + ) { + return rep_vector(m, T); + } } data { @@ -86,7 +95,7 @@ data { matrix[T,K] X; // Regressors vector[K] sigmas; // Scale on seasonality prior real tau; // Scale on changepoints prior - int trend_indicator; // 0 for linear, 1 for logistic + int trend_indicator; // 0 for linear, 1 for logistic, 2 for flat vector[K] s_a; // Indicator of additive features vector[K] s_m; // Indicator of multiplicative features } @@ -104,6 +113,17 @@ parameters { vector[K] beta; // Regressor coefficients } +transformed parameters { + vector[T] trend; + if (trend_indicator == 0) { + trend = linear_trend(k, m, delta, t, A, t_change); + } else if (trend_indicator == 1) { + trend = logistic_trend(k, m, delta, t, cap, A, t_change, S); + } else if (trend_indicator == 2) { + trend = flat_trend(m, T); + } +} + model { //priors k ~ normal(0, 5); @@ -113,19 +133,10 @@ model { beta ~ normal(0, sigmas); // Likelihood - if (trend_indicator == 0) { - y ~ normal( - linear_trend(k, m, delta, t, A, t_change) - .* (1 + X * (beta .* s_m)) - + X * (beta .* s_a), - sigma_obs - ); - } else if (trend_indicator == 1) { - y ~ normal( - logistic_trend(k, m, delta, t, cap, A, t_change, S) - .* (1 + X * (beta .* s_m)) - + X * (beta .* s_a), - sigma_obs - ); - } + y ~ normal( + trend + .* (1 + X * (beta .* s_m)) + + X * (beta .* s_a), + sigma_obs + ); } diff --git a/python/stan/win/prophet.stan b/python/stan/win/prophet.stan index ea9f5b1..b32112e 100644 --- a/python/stan/win/prophet.stan +++ b/python/stan/win/prophet.stan @@ -94,6 +94,17 @@ functions { } return Y; } + + // Flat trend function + + real[] flat_trend( + real m, + int T + ) { + return rep_array(m, T); + } + + } data { @@ -107,7 +118,7 @@ data { real X[T,K]; // Regressors vector[K] sigmas; // Scale on seasonality prior real tau; // Scale on changepoints prior - int trend_indicator; // 0 for linear, 1 for logistic + int trend_indicator; // 0 for linear, 1 for logistic, 2 for flat real s_a[K]; // Indicator of additive features real s_m[K]; // Indicator of multiplicative features } @@ -135,6 +146,8 @@ transformed parameters { trend = linear_trend(k, m, delta, t, A, t_change, S, T); } else if (trend_indicator == 1) { trend = logistic_trend(k, m, delta, t, cap, A, t_change, S, T); + } else if (trend_indicator == 2){ + trend = flat_trend(m, T); } for (i in 1:K) {