timeseries-feature-engineering 0.0.1

A description of your project

Time Series Feature Engineering

Time series feature generator.

Install

pip install timeseries_feature_engineering

How to use

Add Date Parts

df = pd.DataFrame({'date': ['2019-12-04', None, '2019-11-15', '2019-10-24']})
df = add_datepart(df, 'date')
df.head()

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	Year	Month	Week	Day	Dayofweek	Dayofyear	Is_month_end	Is_month_start	Is_quarter_end	Is_quarter_start	Is_year_end	Is_year_start	Elapsed
0	2019.0	12.0	49.0	4.0	2.0	338.0	False	False	False	False	False	False	1575417600
1	NaN	NaN	NaN	NaN	NaN	NaN	False	False	False	False	False	False	None
2	2019.0	11.0	46.0	15.0	4.0	319.0	False	False	False	False	False	False	1573776000
3	2019.0	10.0	43.0	24.0	3.0	297.0	False	False	False	False	False	False	1571875200

Add Moving Average Features

With weighted average.

Recency in an important factor in a time series. Values closer to the current date would hold more information.

df = pd.DataFrame({
    'date': pd.date_range('2019-12-01', '2019-12-10'), 
    'sales': np.random.randint(100, 500, size=10)
})
df = add_moving_average_features(df, 'sales', windows=[3,5], weighted=True)
df.head(10)

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	date	sales	sales_3p_MA	sales_5p_MA
0	2019-12-01	155	NaN	NaN
1	2019-12-02	437	NaN	NaN
2	2019-12-03	361	352.000000	NaN
3	2019-12-04	356	371.166667	NaN
4	2019-12-05	490	423.833333	399.066667
5	2019-12-06	222	333.666667	353.133333
6	2019-12-07	197	254.166667	294.400000
7	2019-12-08	390	297.666667	316.000000
8	2019-12-09	159	242.333333	258.666667
9	2019-12-10	470	353.000000	318.133333

Without weighted average.

df = pd.DataFrame({
    'date': pd.date_range('2019-12-01', '2019-12-10'), 
    'sales': np.random.randint(100, 500, size=10)
})
df = add_moving_average_features(df, 'sales', windows=[3,5], weighted=True)
df.head(10)

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	date	sales	sales_3p_MA	sales_5p_MA
0	2019-12-01	167	NaN	NaN
1	2019-12-02	458	NaN	NaN
2	2019-12-03	260	310.500000	NaN
3	2019-12-04	174	250.000000	NaN
4	2019-12-05	392	297.333333	301.266667
5	2019-12-06	401	360.166667	338.200000
6	2019-12-07	460	429.000000	379.200000
7	2019-12-08	381	410.666667	393.733333
8	2019-12-09	349	378.166667	389.533333
9	2019-12-10	365	362.333333	379.000000

Add Expanding Features

df = pd.DataFrame({
    'date': pd.date_range('2019-12-01', '2019-12-10'), 
    'sales': np.random.randint(100, 500, size=10)
})
df = add_expanding_features(df, 'sales', period=3)
df.head()

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	date	sales	sales_3p_expanding
0	2019-12-01	178	NaN
1	2019-12-02	398	NaN
2	2019-12-03	399	325.0
3	2019-12-04	385	340.0
4	2019-12-05	136	299.2

Add Trend Features

df = pd.DataFrame({
    'date': pd.date_range('2019-12-01', '2019-12-10'), 
    'sales': np.random.randint(100, 500, size=10)
})
df = add_trend_features(df, 'sales', windows=[3,7])
df.head(10)

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	date	sales	sales_3p_trend	sales_7p_trend
0	2019-12-01	237	0.000000	0.000000
1	2019-12-02	388	0.000000	0.000000
2	2019-12-03	384	0.000000	0.000000
3	2019-12-04	498	87.000000	0.000000
4	2019-12-05	275	-37.666667	0.000000
5	2019-12-06	382	-0.666667	0.000000
6	2019-12-07	132	-122.000000	0.000000
7	2019-12-08	337	20.666667	14.285714
8	2019-12-09	496	38.000000	15.428571
9	2019-12-10	216	28.000000	-24.000000