import pandas as pd
from aeon.forecasting.trend import TrendForecaster

y = pd.Series([20.0, 40.0, 60.0, 80.0, 100.0])
>>> 0     20.0
>>> 1     40.0
>>> 2     60.0
>>> 3     80.0
>>> 4    100.0
>>> dtype: float64

forecaster = TrendForecaster()
forecaster.fit(y)  # fit the forecaster
>>> TrendForecaster()

pred = forecaster.predict(fh=[1, 2, 3])  # forecast the next 3 values
>>> 5    120.0
>>> 6    140.0
>>> 7    160.0
>>> dtype: float64

import numpy as np
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier

X = [[[1, 2, 3, 4, 5, 5]],  # 3D array example (univariate)
     [[1, 2, 3, 4, 4, 2]],  # Three samples, one channel, six series length,
     [[8, 7, 6, 5, 4, 4]]]
y = ['low', 'low', 'high']  # class labels for each sample
X = np.array(X)
y = np.array(y)

clf = KNeighborsTimeSeriesClassifier(distance="dtw")
clf.fit(X, y)  # fit the classifier on train data
>>> KNeighborsTimeSeriesClassifier()

X_test = np.array(
    [[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
y_pred = clf.predict(X_test)  # make class predictions on new data
>>> ['low' 'high' 'high']