import numpy as np import pandas as pd from numpy.testing import (assert_almost_equal, assert_equal, assert_raises, assert_allclose) from statsmodels.tsa.seasonal import seasonal_decompose from pandas import DataFrame, date_range class TestDecompose: @classmethod def setup_class(cls): # even data = [-50, 175, 149, 214, 247, 237, 225, 329, 729, 809, 530, 489, 540, 457, 195, 176, 337, 239, 128, 102, 232, 429, 3, 98, 43, -141, -77, -13, 125, 361, -45, 184] cls.data = DataFrame(data, date_range(start='1/1/1951', periods=len(data), freq='Q')) def test_ndarray(self): res_add = seasonal_decompose(self.data.values, freq=4) seasonal = [62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25] trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75, 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12, 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12, 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25, 103.00, 131.62, np.nan, np.nan] random = [np.nan, np.nan, 78.254, 70.254, -36.710, -94.299, -6.371, -62.246, 105.415, 103.576, 2.754, 1.254, 15.415, -10.299, -33.246, -27.746, 46.165, -57.924, 28.004, -36.746, -37.585, 151.826, -75.496, 86.254, -10.210, -194.049, 48.129, 11.004, -40.460, 143.201, np.nan, np.nan] assert_almost_equal(res_add.seasonal, seasonal, 2) assert_almost_equal(res_add.trend, trend, 2) assert_almost_equal(res_add.resid, random, 3) res_mult = seasonal_decompose(np.abs(self.data.values), 'm', freq=4) seasonal = [1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931] trend = [np.nan, np.nan, 171.62, 204.00, 221.25, 245.12, 319.75, 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12, 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12, 191.00, 166.88, 107.25, 80.50, 79.12, 78.75, 116.50, 140.00, 157.38, np.nan, np.nan] random = [np.nan, np.nan, 1.29263, 1.51360, 1.03223, 0.62226, 1.04771, 1.05139, 1.20124, 0.84080, 1.28182, 1.28752, 1.08043, 0.77172, 0.91697, 0.96191, 1.36441, 0.72986, 1.01171, 0.73956, 1.03566, 1.44556, 0.02677, 1.31843, 0.49390, 1.14688, 1.45582, 0.16101, 0.82555, 1.47633, np.nan, np.nan] assert_almost_equal(res_mult.seasonal, seasonal, 4) assert_almost_equal(res_mult.trend, trend, 2) assert_almost_equal(res_mult.resid, random, 4) # test odd res_add = seasonal_decompose(self.data.values[:-1], freq=4) seasonal = [68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66] trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75, 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12, 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12, 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25, 103.00, np.nan, np.nan] random = [np.nan, np.nan, 72.538, 64.538, -42.426, -77.150, -12.087, -67.962, 99.699, 120.725, -2.962, -4.462, 9.699, 6.850, -38.962, -33.462, 40.449, -40.775, 22.288, -42.462, -43.301, 168.975, -81.212, 80.538, -15.926, -176.900, 42.413, 5.288, -46.176, np.nan, np.nan] assert_almost_equal(res_add.seasonal, seasonal, 2) assert_almost_equal(res_add.trend, trend, 2) assert_almost_equal(res_add.resid, random, 3) def test_pandas(self): res_add = seasonal_decompose(self.data, freq=4) freq_override_data = self.data.copy() freq_override_data.index = date_range(start='1/1/1951', periods=len(freq_override_data), freq='A') res_add_override = seasonal_decompose(freq_override_data, freq=4) seasonal = [62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25] trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75, 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12, 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12, 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25, 103.00, 131.62, np.nan, np.nan] random = [np.nan, np.nan, 78.254, 70.254, -36.710, -94.299, -6.371, -62.246, 105.415, 103.576, 2.754, 1.254, 15.415, -10.299, -33.246, -27.746, 46.165, -57.924, 28.004, -36.746, -37.585, 151.826, -75.496, 86.254, -10.210, -194.049, 48.129, 11.004, -40.460, 143.201, np.nan, np.nan] assert_almost_equal(res_add.seasonal.values.squeeze(), seasonal, 2) assert_almost_equal(res_add.trend.values.squeeze(), trend, 2) assert_almost_equal(res_add.resid.values.squeeze(), random, 3) assert_almost_equal(res_add_override.seasonal.values.squeeze(), seasonal, 2) assert_almost_equal(res_add_override.trend.values.squeeze(), trend, 2) assert_almost_equal(res_add_override.resid.values.squeeze(), random, 3) assert_equal(res_add.seasonal.index.values.squeeze(), self.data.index.values) res_mult = seasonal_decompose(np.abs(self.data), 'm', freq=4) res_mult_override = seasonal_decompose(np.abs(freq_override_data), 'm', freq=4) seasonal = [1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931] trend = [np.nan, np.nan, 171.62, 204.00, 221.25, 245.12, 319.75, 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12, 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12, 191.00, 166.88, 107.25, 80.50, 79.12, 78.75, 116.50, 140.00, 157.38, np.nan, np.nan] random = [np.nan, np.nan, 1.29263, 1.51360, 1.03223, 0.62226, 1.04771, 1.05139, 1.20124, 0.84080, 1.28182, 1.28752, 1.08043, 0.77172, 0.91697, 0.96191, 1.36441, 0.72986, 1.01171, 0.73956, 1.03566, 1.44556, 0.02677, 1.31843, 0.49390, 1.14688, 1.45582, 0.16101, 0.82555, 1.47633, np.nan, np.nan] assert_almost_equal(res_mult.seasonal.values.squeeze(), seasonal, 4) assert_almost_equal(res_mult.trend.values.squeeze(), trend, 2) assert_almost_equal(res_mult.resid.values.squeeze(), random, 4) assert_almost_equal(res_mult_override.seasonal.values.squeeze(), seasonal, 4) assert_almost_equal(res_mult_override.trend.values.squeeze(), trend, 2) assert_almost_equal(res_mult_override.resid.values.squeeze(), random, 4) assert_equal(res_mult.seasonal.index.values.squeeze(), self.data.index.values) def test_pandas_nofreq(self, reset_randomstate): # issue #3503 nobs = 100 dta = pd.Series([x % 3 for x in range(nobs)] + np.random.randn(nobs)) res_np = seasonal_decompose(dta.values, freq=3) res = seasonal_decompose(dta, freq=3) atol = 1e-8 rtol = 1e-10 assert_allclose(res.seasonal.values.squeeze(), res_np.seasonal, atol=atol, rtol=rtol) assert_allclose(res.trend.values.squeeze(), res_np.trend, atol=atol, rtol=rtol) assert_allclose(res.resid.values.squeeze(), res_np.resid, atol=atol, rtol=rtol) def test_filt(self): filt = np.array([1/8., 1/4., 1./4, 1/4., 1/8.]) res_add = seasonal_decompose(self.data.values, filt=filt, freq=4) seasonal = [62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38, -60.25] trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75, 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12, 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12, 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25, 103.00, 131.62, np.nan, np.nan] random = [np.nan, np.nan, 78.254, 70.254, -36.710, -94.299, -6.371, -62.246, 105.415, 103.576, 2.754, 1.254, 15.415, -10.299, -33.246, -27.746, 46.165, -57.924, 28.004, -36.746, -37.585, 151.826, -75.496, 86.254, -10.210, -194.049, 48.129, 11.004, -40.460, 143.201, np.nan, np.nan] assert_almost_equal(res_add.seasonal, seasonal, 2) assert_almost_equal(res_add.trend, trend, 2) assert_almost_equal(res_add.resid, random, 3) def test_one_sided_moving_average_in_stl_decompose(self): res_add = seasonal_decompose(self.data.values, freq=4, two_sided=False) seasonal = np.array([76.76, 90.03, -114.4, -52.4, 76.76, 90.03, -114.4, -52.4, 76.76, 90.03, -114.4, -52.4, 76.76, 90.03, -114.4, -52.4, 76.76, 90.03, -114.4, -52.4, 76.76, 90.03, -114.4, -52.4, 76.76, 90.03, -114.4, -52.4, 76.76, 90.03, -114.4, -52.4]) trend = np.array([np.nan, np.nan, np.nan, np.nan, 159.12, 204., 221.25, 245.12, 319.75, 451.5, 561.12, 619.25, 615.62, 548., 462.12, 381.12, 316.62, 264., 228.38, 210.75, 188.38, 199., 207.12, 191., 166.88, 72., -9.25, -33.12, -36.75, 36.25, 103., 131.62]) resid = np.array([np.nan, np.nan, np.nan, np.nan, 11.112, -57.031, 118.147, 136.272, 332.487, 267.469, 83.272, -77.853, -152.388, -181.031, -152.728, -152.728, -56.388, -115.031, 14.022, -56.353, -33.138, 139.969, -89.728, -40.603, -200.638, -303.031, 46.647, 72.522, 84.987, 234.719, -33.603, 104.772]) assert_almost_equal(res_add.seasonal, seasonal, 2) assert_almost_equal(res_add.trend, trend, 2) assert_almost_equal(res_add.resid, resid, 3) res_mult = seasonal_decompose(np.abs(self.data.values), 'm', freq=4, two_sided=False) seasonal = np.array([1.1985, 1.5449, 0.5811, 0.6755, 1.1985, 1.5449, 0.5811, 0.6755, 1.1985, 1.5449, 0.5811, 0.6755, 1.1985, 1.5449, 0.5811, 0.6755, 1.1985, 1.5449, 0.5811, 0.6755, 1.1985, 1.5449, 0.5811, 0.6755, 1.1985, 1.5449, 0.5811, 0.6755, 1.1985, 1.5449, 0.5811, 0.6755]) trend = np.array([np.nan, np.nan, np.nan, np.nan, 171.625, 204., 221.25, 245.125, 319.75, 451.5, 561.125, 619.25, 615.625, 548., 462.125, 381.125, 316.625, 264., 228.375, 210.75, 188.375, 199., 207.125, 191., 166.875, 107.25, 80.5, 79.125, 78.75, 116.5, 140., 157.375]) resid = np.array([np.nan, np.nan, np.nan, np.nan, 1.2008, 0.752, 1.75, 1.987, 1.9023, 1.1598, 1.6253, 1.169, 0.7319, 0.5398, 0.7261, 0.6837, 0.888, 0.586, 0.9645, 0.7165, 1.0276, 1.3954, 0.0249, 0.7596, 0.215, 0.851, 1.646, 0.2432, 1.3244, 2.0058, 0.5531, 1.7309]) assert_almost_equal(res_mult.seasonal, seasonal, 4) assert_almost_equal(res_mult.trend, trend, 2) assert_almost_equal(res_mult.resid, resid, 4) # test odd res_add = seasonal_decompose(self.data.values[:-1], freq=4, two_sided=False) seasonal = np.array([81.21, 94.48, -109.95, -65.74, 81.21, 94.48, -109.95, -65.74, 81.21, 94.48, -109.95, -65.74, 81.21, 94.48, -109.95, -65.74, 81.21, 94.48, -109.95, -65.74, 81.21, 94.48, -109.95, -65.74, 81.21, 94.48, -109.95, -65.74, 81.21, 94.48, -109.95]) trend = [np.nan, np.nan, np.nan, np.nan, 159.12, 204., 221.25, 245.12, 319.75, 451.5, 561.12, 619.25, 615.62, 548., 462.12, 381.12, 316.62, 264., 228.38, 210.75, 188.38, 199., 207.12, 191., 166.88, 72., -9.25, -33.12, -36.75, 36.25, 103.] random = [np.nan, np.nan, np.nan, np.nan, 6.663, -61.48, 113.699, 149.618, 328.038, 263.02, 78.824, -64.507, -156.837, -185.48, -157.176, -139.382, -60.837, -119.48, 9.574, -43.007, -37.587, 135.52, -94.176, -27.257, -205.087, -307.48, 42.199, 85.868, 80.538, 230.27, -38.051] assert_almost_equal(res_add.seasonal, seasonal, 2) assert_almost_equal(res_add.trend, trend, 2) assert_almost_equal(res_add.resid, random, 3) def test_2d(self): x = np.tile(np.arange(6), (2, 1)).T trend = seasonal_decompose(x, freq=2).trend expected = np.tile(np.arange(6, dtype=float), (2, 1)).T expected[0] = expected[-1] = np.nan assert_equal(trend, expected) def test_interpolate_trend(self): x = np.arange(12) freq = 4 trend = seasonal_decompose(x, freq=freq).trend assert_equal(trend[0], np.nan) trend = seasonal_decompose(x, freq=freq, extrapolate_trend=5).trend assert_almost_equal(trend, x) trend = seasonal_decompose(x, freq=freq, extrapolate_trend='freq').trend assert_almost_equal(trend, x) trend = seasonal_decompose(x[:, None], freq=freq, extrapolate_trend=5).trend assert_almost_equal(trend, x) # 2d case x = np.tile(np.arange(12), (2, 1)).T trend = seasonal_decompose(x, freq=freq, extrapolate_trend=1).trend assert_almost_equal(trend, x) trend = seasonal_decompose(x, freq=freq, extrapolate_trend='freq').trend assert_almost_equal(trend, x) def test_raises(self): assert_raises(ValueError, seasonal_decompose, self.data.values) assert_raises(ValueError, seasonal_decompose, self.data, 'm', freq=4) x = self.data.astype(float).copy() x.iloc[2] = np.nan assert_raises(ValueError, seasonal_decompose, x)