srinidhi-patil / giotto-time Goto Github PK

from gtime import *
from gtime.feature_extraction import *
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

# Create random DataFrame with DatetimeIndex
X_dt = pd.DataFrame(np.random.randint(4, size=(20)),
                    index=pd.date_range("2019-12-20", "2020-01-08"),
                    columns=['time_series'])

# Convert the DatetimeIndex to PeriodIndex and create y matrix
X = preprocessing.TimeSeriesPreparation().transform(X_dt)
y = model_selection.horizon_shift(X, horizon=2)

# Create some features
cal = feature_generation.Calendar(region="europe", country="Switzerland", kernel=np.array([1, 2]))
X_f = compose.FeatureCreation(
    [('s_2', Shift(2), ['time_series']),
     ('ma_3', MovingAverage(window_size=3), ['time_series']),
     ('cal', cal, ['time_series'])]).fit_transform(X)

# Train/test split
X_train, y_train, X_test, y_test = model_selection.FeatureSplitter().transform(X_f, y)

# Try sklearn's MultiOutputRegressor as time-series forecasting model
gar = forecasting.GAR(LinearRegression())
gar.fit(X_train, y_train).predict(X_test)