Save and load forecaster¶

Skforecast models can be loaded and stored using pickle or joblib library. A simple example using joblib is shown below.

Libraries¶

In [1]:

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

# Libraries # ============================================================================== import numpy as np import pandas as pd from joblib import dump, load 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')

# 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')

Save and load model¶

In [3]:

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

forecaster.fit(y=data['y'])
forecaster.predict(steps=3)

# Create and train forecaster forecaster = ForecasterAutoreg( regressor = RandomForestRegressor(random_state=123), lags = 5 ) forecaster.fit(y=data['y']) forecaster.predict(steps=3)

Out[3]:

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

In [4]:

# Save model
dump(forecaster, filename='forecaster.py')

# Save model dump(forecaster, filename='forecaster.py')

Out[4]:

['forecaster.py']

In [5]:

# Load model
forecaster_loaded = load('forecaster.py')

# Load model forecaster_loaded = load('forecaster.py')

In [6]:

# Predict
forecaster_loaded.predict(steps=3)

# Predict forecaster_loaded.predict(steps=3)

Out[6]:

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

In [7]:

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