time-decomp

Time series decomposition plot trend and seasonality

Plot trend and seasonality together in one chart as described at Business Days Time Series Weekly Trend and Seasonality.

Added SARIMAX keew predictions (see sarimaxtest.py testplot_keew())

Example usage from ./Python/tests/keewdecomptest.py

```

create test

test_decomposition.py

import unittest import pandas as pd import numpy as np import matplotlib.pyplot as plt from time_decomp.decomposition import DecompositionSingleton

class TestKeewDecomposition(unittest.TestCase): def setUp(self): self.decomp = DecompositionSingleton()

    n = 2000

    df = pd.DataFrame({'A': np.random.randint(0,100, size=(n,)), 'B': np.random.randint(0,100, size=(n,))})

    lsDays = [pd.Timestamp(2021, 1, 1)]*n
    for i in range(n):
        # construct time, where iYear-iMonth-i
        lsDays[i] = pd.Timestamp( np.random.randint(2021,2025), np.random.randint(1,13) , (i+1) % 28 + 1)    

    df['Day'] = lsDays

    df['Year'] = df['Day'].dt.year
    df['Month'] = df['Day'].dt.month
    df['KeewMonth'] = df['Day'].apply(self.decomp.get_month_keew)
    df['Keew']=(df['Month']-1)*4+df['KeewMonth']

    self.decomp.df = df.groupby(['Year', 'Keew']).last().reset_index()

    self.decomp.features = ['A', 'B']
    self.decomp.decompose_params = {'model': 'additive', 'period':48, 'extrapolate_trend':'freq'}        

def test_plot_decomposition(self):

    # output df info
    print("Starting test_plot_decomposition")
    print("DataFrame Info:")
    print(self.decomp.df.info())
    print("DataFrame Head:")
    print("\n%s", self.decomp.df.head())


    self.decomp.m_decompose()
    self.decomp.plot_decomposition('A', 'Year', range(2021,2025), 'Keew', 'A keew', chart_elements=[self.decomp.ChartElement.TREND, self.decomp.ChartElement.SEASONAL])
    plt.show()

```

Added PLOTLY to run from IPython Notebook

``` from plotly.offline import iplot

decomp.mdecompose() fig = decomp.plotdecompositionplotly('A', 'Year', range(2021,2025), 'Keew', 'A keew', chartelements=[ decomp.ChartElement.OBSERVED, decomp.ChartElement.SEASONAL]) iplot(fig) ```

Added trends analysis from EnvironmentalTrends

``` from time_decomp.environmentaltrends import EnvironmentalTrends

Set pandas option to display all columns

pd.setoption('display.maxcolumns', None)

class TestEnvironmentalTrends(unittest.TestCase):

def setUp(self):
    self.decomp = EnvironmentalTrends()
    self.decomp.features = ['A']
    self.decomp.trend_data_params = {'year_col':'Year', 'month_col':'Month' }
    self.decomp.trends_params = {'seasons_per_year': 12, 'trend_lengths': [1], 'end_years': [2025]}

def test_m_trends(self):
    self.decomp.m_trends()
    # output df info
    print("Starting test_plot_decomposition")
    print("DataFrame Info:")
    print(self.decomp.t['A'].info())
    print("DataFrame Head:")
    print("\n%s", self.decomp.t['A'].head())

```

``` DataFrame Head:

%s Frequency SeasonsPeryear TrendLength TrendEnd TrendPeriod \ 0 Monthly 12 1 2025 Jul 2024 to Jun 2025

ValueCount Minimum Median Average Maximum YearsInPeriod \ 0 12.0 5.0 48.0 50.333333 96.0 1.0

SeasonsInPeriod PercentOfYears PercentOfSeasons KW-pValue Seasonality \ 0 12.0 100.0 100.0 0.443263 Non-seasonal

AppliedSeasonality MK-S MK-Variance MK-pvalue IncreasingLikelihood \ 0 Non-seasonal 8.0 212.666667 0.631222 68.438909

  TrendDirection  SenSlope  LowerSlope  UpperSlope  

0 Likely increasing 16.5 -384.0 544.8 ```