Getting Started#

The following information is designed to get users up and running with aeon quickly. If installation is required, please see our installation guide for installing aeon.

We assume basic familiarity with the scikit-learn package. If you are confused, you may want to view their getting started guides.

aeon is an open source toolkit for learning from time series. It provides access to the very latest algorithms for time series machine learning, in addition to a range of classical techniques for the following learning tasks:

Additionally, it provides numerous algorithms for time series transformation, altering time series into different representations and domains or processing time series data into tabular data.

The following provides introductory examples for each of these modules. In the examples used the datatypes most commonly used for the task, but a variety of input types for data are available. See here for more information on input datatypes. For more information on the variety of estimators available for each task, see the API and examples pages.

Time Series Data#

A time series is a series of real valued data assumed to be ordered. A univariate time series is a singular series, where each observation is a single value. For example, the heartbeat ECG reading from a single sensor or the number of passengers using an airline per month would form a univariate series.

>>> from aeon.datasets import load_airline
>>> y = load_airline()  # load an example univariate series with timestamps
>>> y.head()
Period
1960-08    606.0
1960-09    508.0
1960-10    461.0
1960-11    390.0
1960-12    432.0
Freq: M, Name: Number of airline passengers, dtype: float64

A multivariate time series is made up of multiple series, where each observation is a vector of related recordings in the same time index. An examples would be a motion trace of from a smartwatch with at least three dimensions (X,Y,Z co-ordinates), or multiple financial statistics recorded over time. Single multivariate series input typically follows the shape (n_timepoints, n_channels).

>>> from aeon.datasets import load_uschange
>>> y, X = load_uschange("Quarter")  # load an example multivariate series
>>> X.set_index(y).head()
         Consumption    Income  Production   Savings  Unemployment
Quarter
1970 Q1     0.615986  0.972261   -2.452700  4.810312           0.9
1970 Q2     0.460376  1.169085   -0.551525  7.287992           0.5
1970 Q3     0.876791  1.553271   -0.358708  7.289013           0.5
1970 Q4    -0.274245 -0.255272   -2.185455  0.985230           0.7
1971 Q1     1.897371  1.987154    1.909734  3.657771          -0.1

We commonly refer to the number of observations for a time series as n_timepoints or series_length. If a series is multivariate, we refer to the dimensions as channels (to avoid confusion with the dimensions of array) and in code use n_channels. Dimensions may also be referred to as variables.

Different parts of aeon work with single series or collections of series. The forecasting and annotation modules will commonly use single series input, while classification, regression and clustering modules will use collections of time series. Collections of time series may also be referred to a Panels. Collections of time series will often be accompanied by an array of target variables.

>>> from aeon.datasets import load_italy_power_demand
>>> X, y = load_italy_power_demand()  # load an example univariate collection
>>> X.shape
(1096, 1, 24)
>>> X[:5, :, :5]
[[[-0.71051757 -1.1833204  -1.3724416  -1.5930829  -1.4670021 ]]
 [[-0.99300935 -1.4267865  -1.5798843  -1.6054006  -1.6309169 ]]
 [[ 1.3190669   0.56977448  0.19512825 -0.08585642 -0.17951799]]
 [[-0.81244429 -1.1575534  -1.4163852  -1.5314215  -1.5026624 ]]
 [[-0.97284033 -1.3905178  -1.5367049  -1.6202404  -1.6202404 ]]]
>>> y[:5]
['1' '1' '2' '2' '1']

We use the terms case or instance when referring to a single time series contained in a collection. The size of a collection of time series is referred to as n_cases or n_instances. Collections of time typically follows the shape (n_cases, n_channels, n_timepoints) if the series are equal length, but n_timepoints may vary between cases.

The datatypes used by modules also differ to match the use case. Module focusing on single series use cases will commonly use pandas DataFrame and Series objects to store time series data as shown in the first two examples. Modules focusing on collections on time series will commonly use numpy arrays or lists of arrays to store time series data.

>>>from aeon.datasets import load_basic_motions, load_plaid, load_japanese_vowels
>>> X2, y2 = load_basic_motions() # example equal length multivariate collection
>>> X2.shape
(80, 6, 100)
>>> X3, y3 = load_plaid()  # example unequal length univariate collection
>>> type(X3)
<class 'list'>
>>> len(X3)
1074
>>> X3[0].shape
(1, 500)
>>> X4, y4 = load_japanese_vowels()  # example unequal length mutlivariate collection
>>> len(X4)
640
>>> X4[0].shape
(12, 20)

Forecasting#

The possible use cases for forecasting are more complex than with the other modules. Like scikit-learn, forecasters use a fit and predict model, but the arguments are different. The simplest forecasting use case is when you have a single series and you want to build a model on that series (e.g. ARMA model) to predict values in the future. At their most basic, forecasters require a series to forecast for fit, and a forecast horizon (fh) to specify how many time steps ahead to make a forecast in predict. This code fits a TrendForecaster on our loaded data and predicts the next value in the series.

>>> from aeon.datasets import load_airline
>>> from aeon.forecasting.trend import TrendForecaster
>>> y = load_airline()
>>> forecaster = TrendForecaster()
>>> forecaster.fit(y)  # fit the forecaster
TrendForecaster()
>>> pred = forecaster.predict(fh=1)  # predict the next value
1961-01    472.944444
Freq: M, dtype: float64

An integer fh value will forecast n points into the future, i.e. a value of 3 will make a prediction for 1961-03. You can predict multiple points into the future by passing a list of integers to fh.

>>> pred = forecaster.predict(fh=[1,2,3])  # predict the next 3 values
1961-01    472.944444
1961-02    475.601628
1961-03    478.258812
Freq: M, dtype: float64

You can split a series into train and test partitions. This code splits airline into two parts, builds the forecasting model on the train portion and makes forecasts for the time points represented in the test segment. In this example we use a ForecastingHorizon object to input our desired forecast horizon using the Series index.

>>> from aeon.forecasting.model_selection import temporal_train_test_split
>>> from aeon.forecasting.base import ForecastingHorizon
>>> from aeon.performance_metrics.forecasting import mean_absolute_percentage_error
>>> y_train, y_test = temporal_train_test_split(y)  # split the data into train and test series
>>> fh = ForecastingHorizon(y_test.index, is_relative=False)
>>> forecaster.fit(y_train)  # fit the forecaster on train series
>>> y_pred = forecaster.predict(fh)  # predict the test series time stamps
>>> mean_absolute_percentage_error(y_test, y_pred)
0.11725953222644162

aeon has a rich functionality for forecasting, and supports a wide variety of use cases. To find out more about forecasting in aeon, you can explore through the extensive user guide notebook.

Time Series Classification (TSC)#

Classification generally use numpy arrays to store time series. We recommend storing time series for classification in 3D numpy arrays even if each time series is univariate. Classifiers will work with 2D input as you would expect from scikit-learn, but other packages may treat 2D input as a single multivariate series. This is the case for non-collection transformers, and you may find unexpected outputs if you input a 2D array treating it as multiple time series.

Note we assume series length is always the same for all channels of a single series regardless of input type. The target variable should be a numpy array of type float, int or str.

The classification estimator interface should be familiar if you have worked with scikit-learn. In this example we fit a KNeighborsTimeSeriesClassifier with dynamic time warping (dtw) on our example data.

>>> import numpy as np
>>> from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
>>> X = [[[1, 2, 3, 4, 5, 6, 7]],  # 3D array example (univariate)
...      [[4, 4, 4, 5, 6, 7, 3]]]  # Two samples, one channel, seven series length
>>> y = [0, 1]  # 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, 2], [4, 4, 4, 4, 4, 4, 4]])
>>> clf.predict(X_test)  # make class predictions on new data
[0 1]

Once the classifier has been fit using the training data and class labels, we can predict the labels for new cases. Like scikit-learn, predict_proba methods are available to predict class probabilities and a score method is present to calculate accuracy on new data.

All aeon classifiers can be used with scikit-learn functionality for e.g. model evaluation, parameter searching and pipelines. Explore the wide range of algorithm types available in aeon in the classification notebooks.

Time Series Extrinsic Regression (TSER)#

Time series extrinsic regression assumes that the target variable is continuous rather than discrete, as for classification. The same input data considerations apply from the classification section, and the modules function similarly.

The target variable should be a numpy array of type float.

“Time series regression” is a term commonly used in forecasting. To avoid confusion, the term “time series extrinsic regression” is commonly used to refer to the traditional machine learning regression task but for time series data.

In the following example we use a KNeighborsTimeSeriesRegressor on an example time series extrinsic regression problem called Covid3Month.

>>> from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
>>> from aeon.datasets import load_covid_3month
>>> from sklearn.metrics import mean_squared_error
>>> X_train, y_train = load_covid_3month(split="train")
>>> X_test, y_test = load_covid_3month(split="test")
>>> reg = KNeighborsTimeSeriesRegressor(distance="dtw")
>>> reg.fit(X_train, y_train)  # fit the regressor on train data
KNeighborsTimeSeriesRegressor()
>>> y_pred = reg.predict(X_test)  # make label predictions on new data
>>> y_pred[:6]
[0.04218472 0.01459854 0.         0.0164468  0.06254257 0.11111111]
>>> mean_squared_error(y_test, y_pred)
0.002921957478363366

Time Series Clustering (TSCL)#

Like classification and regression, time series clustering aims to follow the scikit-learn interface where possible. The same input data format is used as the modules. This example fits a TimeSeriesKMeans clusterer on the ArrowHead dataset.

>>> from aeon.clustering.k_means import TimeSeriesKMeans
>>> from aeon.datasets import load_arrow_head
>>> from sklearn.metrics import rand_score
>>> X, y = load_arrow_head()
>>> kmeans = TimeSeriesKMeans(n_clusters=3, metric="dtw")
>>> kmeans.fit(X) # fit the clusterer
TimeSeriesKMeans(n_clusters=3)
>>> kmeans.labels_[0:10]  # cluster labels
[2 1 1 0 1 1 0 1 1 0]
>>> rand_score(y, kmeans.labels_)
0.6377792823290453

After calling fit, the labels_ attribute contains the cluster labels for each time series. The predict method can be used to predict the cluster labels for new data.

Time Series Annotation#

Annotation encompasses a range of time series tasks, including segmentation and anomaly detection. The package is still in early development, so major changes are expected as time goes on.

>>> from aeon.annotation.adapters import PyODAnnotator
>>> from aeon.datasets import load_airline
>>> from pyod.models.iforest import IForest
>>> y = load_airline()
>>> pyod_model = IForest()
>>> annotator = PyODAnnotator(pyod_model)
>>> annotator.fit(y)
>>> annotated_series = annotator.predict(y)
>>> annotated_series.head()
1949-01    1
1949-02    0
1949-03    0
1949-04    0
1949-05    0
Freq: M, dtype: int32

Transformers for Time Series Data#

Coming soon!

Transformers for Collections of Time Series#

Coming soon!

Pipelines for aeon estimators#

Like scikit-learn, aeon provides pipeline classes which can be used to chain transformations and estimators together. The simplest pipeline for forecasting is the TransformedTargetForecaster.

In the following example, we chain together a BoxCoxTransformer, Deseasonalizer and ARIMA forecaster to make a forecast (if you want to run this yourself, you will need to pip install statsmodels and pip install pmdarima).

>>> import numpy as np
>>> from aeon.datasets import load_airline
>>> from aeon.transformations.series.boxcox import BoxCoxTransformer
>>> from aeon.transformations.series.detrend import Deseasonalizer
>>> from aeon.forecasting.arima import ARIMA
>>> from aeon.forecasting.compose import TransformedTargetForecaster
...
>>> # Load airline data
>>> y = load_airline()
>>> # Create and fit the pipeline
>>> pipe = TransformedTargetForecaster(
...     steps=[
...         ("boxcox", BoxCoxTransformer(sp=12)),
...         ("deseasonaliser", Deseasonalizer(sp=12)),
...         ("arima", ARIMA(order=(1, 1, 0))),
...     ]
... )
>>> pipe.fit(y)
>>> # Make predictions
>>> pipe.predict(fh=np.arange(1, 13))
1961-01    442.440026
1961-02    433.548016
1961-03    493.371215
1961-04    484.284090
1961-05    490.850617
1961-06    555.134680
1961-07    609.581248
1961-08    611.345923
1961-09    542.610868
1961-10    482.452172
1961-11    428.885045
1961-12    479.297989
Freq: M, dtype: float64

For most learning tasks including forecasting, the aeon make_pipeline function can be used to creating pipelines as well.

>>> from aeon.pipeline import make_pipeline
>>> make_pipeline(
...     BoxCoxTransformer(sp=12), Deseasonalizer(sp=12), ARIMA(order=(1, 1, 0))
... )
TransformedTargetForecaster(steps=[BoxCoxTransformer(sp=12),
                                   Deseasonalizer(sp=12),
                                   ARIMA(order=(1, 1, 0))])

For machine learning tasks such as classification, regression and clustering, the scikit-learn make_pipeline functionality can be used.

The following example uses the Catch22 feature extraction transformer and a random forest classifier to classify.

>>> from aeon.datasets import load_italy_power_demand
>>> from aeon.transformations.panel import Catch22
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.metrics import accuracy_score
...
>>> # Load the italy power demand dataset
>>> X_train, y_train = load_italy_power_demand(split="train")
>>> X_test, y_test = load_italy_power_demand(split="test")
...
>>> # Create and fit the pipeline
>>> pipe = make_pipeline(
...     Catch22(replace_nans=True),
...     RandomForestClassifier(random_state=42),
... )
>>> pipe.fit(X_train, y_train)
Pipeline(steps=[('catch22', Catch22(replace_nans=True)),
                ('randomforestclassifier',
                 RandomForestClassifier(random_state=42))])
>>> # Make predictions like any other sklearn estimator
>>> accuracy_score(pipe.predict(X_test), y_test)
0.8989310009718173

Parameter searching for aeon estimators#

Tools for selecting parameter values for aeon estimators are available. In the following example, we use a ForecastingGridSearchCV to ARIMA order values for the forecasting pipeline we created in the previous example.

>>> import warnings
>>> import numpy as np
>>> from itertools import product
>>> from sklearn.exceptions import ConvergenceWarning
>>> from aeon.datasets import load_airline
>>> from aeon.forecasting.compose import TransformedTargetForecaster
>>> from aeon.forecasting.model_selection import (
...     ExpandingWindowSplitter,
...     ForecastingGridSearchCV,
... )
>>> from aeon.forecasting.arima import ARIMA
>>> from aeon.transformations.series.boxcox import BoxCoxTransformer
>>> from aeon.transformations.series.detrend import Deseasonalizer
...
>>> y = load_airline()
...
>>> cv = ExpandingWindowSplitter(initial_window=120, fh=np.arange(1, 13))
>>> arima_orders = list(product((0, 1, 2), (0, 1, 2), (0, 1, 2)))
...
>>> warnings.simplefilter("ignore", category=ConvergenceWarning)
>>> gscv = ForecastingGridSearchCV(
...     forecaster=TransformedTargetForecaster(
...         steps=[
...             ("boxcox", BoxCoxTransformer(sp=12)),
...             ("deseasonaliser", Deseasonalizer(sp=12)),
...             ("arima", ARIMA(order=(1, 1, 0))),
...        ]
...     ),
...     param_grid={"arima__order": arima_orders},
...     cv=cv,
... )
>>> gscv.fit(y)
...
>>> gscv.predict(fh=np.arange(1, 13))
1961-01    443.073816
1961-02    434.309107
1961-03    494.198070
1961-04    485.105623
1961-05    491.684116
1961-06    556.064082
1961-07    610.591655
1961-08    612.362761
1961-09    543.533022
1961-10    483.289701
1961-11    429.645587
1961-12    480.137248
Freq: M, dtype: float64
>>> gscv.best_params_["arima__order"]
(0, 1, 1)

Like with pipelines, tasks such as classification, regression and clustering can use the available scikit-learn functionality.

>>> from sklearn.metrics import accuracy_score
>>> from sklearn.model_selection import GridSearchCV, KFold
>>> from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
>>> from aeon.datasets import load_italy_power_demand
...
>>> # Load the italy power demand dataset
>>> X_train, y_train = load_italy_power_demand(split="train")
>>> X_test, y_test = load_italy_power_demand(split="test")
...
>>> knn = KNeighborsTimeSeriesClassifier()
>>> param_grid = {"n_neighbors": [1, 5], "distance": ["euclidean", "dtw"]}
...
>>> gscv = GridSearchCV(knn, param_grid, cv=KFold(n_splits=4))
>>> gscv.fit(X_train, y_train)
...
>>> y_pred = gscv.predict(X_test)
>>> y_pred[:6]
['2' '2' '2' '2' '2' '1']
>>> accuracy_score(y_test, y_pred)
0.9523809523809523
>>> gscv.best_params_
{'distance': 'euclidean', 'n_neighbors': 5}