Input data¶

Since Version 0.4.0 only pandas series and dataframes are allowed (although internally numpy arrays are used for performance). Based on the type of pandas index, the following rules are applied:

• If the index is not of type DatetimeIndex, a RangeIndex is created.

• If the index is of type DatetimeIndex and but has no frequency, a RangeIndex is created.

• If the index is of type DatetimeIndex and has a frequency, nothing is changed.

  Note

There is nothing wrong with using data that does not have an associated date/time index. However, if a pandas series with an associated frequency is used, results will have a more useful index.

Libraries¶

In [1]:

# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from skforecast.ForecasterAutoreg import ForecasterAutoreg

# Libraries # ============================================================================== import numpy as np import pandas as pd from sklearn.ensemble import RandomForestRegressor from skforecast.ForecasterAutoreg import ForecasterAutoreg

Data¶

In [2]:

# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'date'])
data['date'] = pd.to_datetime(data['date'], format='%Y/%m/%d')
data = data.set_index('date')
data = data.asfreq('MS')
data

# Download data # ============================================================================== url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv') data = pd.read_csv(url, sep=',', header=0, names=['y', 'date']) data['date'] = pd.to_datetime(data['date'], format='%Y/%m/%d') data = data.set_index('date') data = data.asfreq('MS') data

Out[2]:

	y
date
1991-07-01	0.429795
1991-08-01	0.400906
1991-09-01	0.432159
1991-10-01	0.492543
1991-11-01	0.502369
...	...
2008-02-01	0.761822
2008-03-01	0.649435
2008-04-01	0.827887
2008-05-01	0.816255
2008-06-01	0.762137

204 rows × 1 columns

Train and predict using input with datetime and frequency index¶

In [3]:

# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor = RandomForestRegressor(random_state=123),
                 lags = 5
             )

forecaster.fit(y=data['y'])

# Predictions
# ==============================================================================
forecaster.predict(steps=5)

# Create and fit forecaster # ============================================================================== forecaster = ForecasterAutoreg( regressor = RandomForestRegressor(random_state=123), lags = 5 ) forecaster.fit(y=data['y']) # Predictions # ============================================================================== forecaster.predict(steps=5)

Out[3]:

2008-07-01    0.714526
2008-08-01    0.789144
2008-09-01    0.818433
2008-10-01    0.845027
2008-11-01    0.914621
Freq: MS, Name: pred, dtype: float64

Train and predict using input without datetime index¶

In [4]:

data = data.reset_index(drop=True)
data.head()

data = data.reset_index(drop=True) data.head()

Out[4]:

	y
0	0.429795
1	0.400906
2	0.432159
3	0.492543
4	0.502369

In [5]:

forecaster.fit(y=data['y'])
forecaster.predict(steps=5)

forecaster.fit(y=data['y']) forecaster.predict(steps=5)

Out[5]:

204    0.714526
205    0.789144
206    0.818433
207    0.845027
208    0.914621
Name: pred, dtype: float64

In [6]:

%%html
<style>
.jupyter-wrapper .jp-CodeCell .jp-Cell-inputWrapper .jp-InputPrompt {display: none;}
</style>

%%html