Python seasonal.seasonal_decompose函数代码示例

    def test_pandas(self):
        res_add = seasonal_decompose(self.data, freq=4)
        freq_override_data = self.data.copy()
        freq_override_data.index = DatetimeIndex(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):
        # 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 make_stationary(self):
     # remove trend and seasonality 
     #for positive trend, to penalize higher values do log/squqreroot/cube root etc...
     self.ts_log = np.log(self.df)
     
     #estimate or model trend, then remove from the series. diff appraoches        
     # aggregation: take avg for monthly/weekly avg
     # smooth: taking rolling avg
     # poly fit : fit a regression model
     
     # Exanoke 1: using smoothing as example, rolling avg
     moving_avg = pd.rolling_mean(self.df,window=287)
     ts_log_moving_avg_diff = self.ts_log - moving_avg
     ts_log_moving_avg_diff.dropna(inplace=True)
     
     # Example 2: using exponential weighted moving avg (EWMA)
     # halflife is same as window, how many datapoint to make up 1 cycle
     expwighted_avg = pd.ewma(self.ts_log, halflife=287)
     ts_log_ewma_diff = self.ts_log - expwighted_avg
     
     # Example 3: differencing: take the difference of the observation at a particular instant 
     # with that at the previous instant
     self.ts_log_diff = self.ts_log - self.ts_log.shift()
     
     # Example 4: decomposing
     # trend and seasonality are modeled separately and the remaining part of the series is returned
     # pandas.DataFrame with index doesn't work, need to pass in numpy value as datafram.values
     decomposition = seasonal_decompose(ts_log.values, freq=288)
     trend = decomposition.trend
     seasonal = decomposition.seasonal
     residual = decomposition.resid

def decompose(df,col,freq):
    "To plot the decomposition graphs "
    decomposed = seasonal_decompose(df[col].values, freq=freq)
    pd.DataFrame(decomposed.observed).plot(figsize=(12,4), title = "Observed")
    pd.DataFrame(decomposed.trend).plot(figsize=(12,4), title = "Trend")
    pd.DataFrame(decomposed.seasonal).plot(figsize=(12,4), title = "Seasonal")
    pd.DataFrame(decomposed.resid).plot(figsize=(12,4), title = "Residuals")

def _create_grid_plot_of_trends(df, X, col_list, filename):

    width  = 600
    height = 400
        
    color_palette = [ 'Black', 'Red', 'Purple', 'Green', 'Brown', 'Yellow', 'Cyan', 'Blue', 'Orange', 'Pink']
    i = 0
    #2 columns, so number of rows is total /2 
    row_index = 0
    row_list = []
    row = []
    for col in col_list[1:]: #skip the date column
        # create a new plot
        s1 = figure(x_axis_type = 'datetime', width=width, plot_height=height, title=col + ' trend')
        #seasonal decompae to extract seasonal trends
        decomposition = seasonal_decompose(np.array(df[col]), model='additive', freq=15)  
        s1.line(X, decomposition.trend, color=color_palette[i % len(color_palette)], alpha=0.5, line_width=2)

        row.append(s1)
        if len(row) == 2:
            row_copy = copy.deepcopy(row)
            row_list.append(row_copy)
            row = []
            i = 0
        i += 1
        

    # put all the plots in a grid layout
    p = gridplot(row_list)

    save(vplot(p), filename=filename, title='trends')  

def decomp(ts):
	decomposition = seasonal_decompose(ts[Y_name])
	fig = decomposition.plot() 
	plt.tight_layout()
	fig.savefig('decomp.png', bbox_inches="tight")
	trend = decomposition.trend
	seasonal = decomposition.seasonal
	resid = decomposition.resid

def seasonal_decompose(timeSeries, freq = 34):
    # Seasonal decomposition using moving averages
    decomposition = tsa_seasonal.seasonal_decompose(timeSeries, freq = freq)
    trend = decomposition.trend
    seasonal = decomposition.seasonal
    residual = decomposition.resid

    return [trend, seasonal, residual]

    def test_interpolate_trend(self):
        x = np.arange(6)
        trend = seasonal_decompose(x, freq=2).trend
        assert_equal(trend[0], np.nan)

        trend = seasonal_decompose(x, freq=2, extrapolate_trend=1).trend
        assert_almost_equal(trend, x)

        trend = seasonal_decompose(x, freq=2, extrapolate_trend='freq').trend
        assert_almost_equal(trend, x)

        # 2d case
        x = np.tile(np.arange(6), (2, 1)).T
        trend = seasonal_decompose(x, freq=2, extrapolate_trend=1).trend
        assert_almost_equal(trend, x)

        trend = seasonal_decompose(x, freq=2, extrapolate_trend='freq').trend
        assert_almost_equal(trend, x)

def decompose_pre(ts):
	ts_log = np.log(ts)
	decomposition = seasonal_decompose(ts_log.values, freq = 24)
	# decomposition.plot()
	# plt.show(block= False)
	ts_log_decompose = ts_log
	ts_log_decompose.plays = decomposition.resid
	# print ts_log_decompose
	ts_log_decompose.dropna(inplace = True)
	stationarity_test(ts_log_decompose)
	return ts_log_decompose

def freq(df,col,max1):
    "To find the required freq for the decompostion "

    count = None
    for i in range(1,max1):
        try:
            decomposed = seasonal_decompose(df[col].values, freq=i)
            decomposed.resid = decomposed.resid[[~np.isnan(decomposed.resid)]]
            print decomposed.resid
        ##decomposed.resid = [1,2,1,2,1,2]
            x = np.array(decomposed.resid)
            z,p = stats.kstest(x,'norm')
            if(p<0.055):
              print 'It is not the required freq'
            else:
                print 'it is the required freq'
                count = i
        except ValueError:
            pass
    decompose(df,col,i)
    return count

 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)

# fontsize is just for the axes size
unq_rel_cnts1['distinct_freq'].loc[:].plot(figsize=(40,8), fontsize=30)  


# #### Execute some Univariate Statistics

# In[17]:


unq_rel_cnts1['distinct_freq'].describe()


# In[18]:


decomposition = seasonal_decompose(unq_rel_cnts1['distinct_freq'].values,freq=24 )  
  
fig = decomposition.plot()  
fig.set_size_inches(15, 8)


# In[19]:


# Graph Autocorrelation and Partial Autocorrelation data
fig, axes = plt.subplots(1, 2, figsize=(15,4))

fig = sm.graphics.tsa.plot_acf(unq_rel_cnts1['distinct_freq'], lags=12, ax=axes[0])
fig = sm.graphics.tsa.plot_pacf(unq_rel_cnts1['distinct_freq'], lags=12, ax=axes[1])

    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_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 diagnostics():
    decomposition = seasonal_decompose(view_hour['distinct_freq_sum'].values,freq=24 )  
      
    fig = decomposition.plot()  
    fig.set_size_inches(50, 8)