slearn.slearn

class slearn.slearn(method='fABBA', ws=1, step=10, classifier_name='MLPClassifier', form='numeric', random_seed=0, verbose=1)

A package linking symbolic representation with scikit-learn for time series prediction.

classifier_name - str, default=MLPClassifier,

optional choices = {“KNeighborsClassifier”, “GaussianProcessClassifier” “QuadraticDiscriminantAnalysis”, “DecisionTreeClassifier”, “LogisticRegression”, “AdaBoostClassifier”, “RandomForestClassifier”, “GaussianNB”, “LGBM”, “SVC”, “RBF”}:

The classifier you specify for symbols prediction.

ws - int, default=3:

The windows size for symbols to be the features, i.e, the dimensions of features.

step - int, default=1,

The number of symbols for prediction.

method - str {‘SAX’, ‘ABBA’, ‘fABBA’}:

The symbolic time series representation. We use fABBA for ABBA method.

form - str, default=’numeric’:

predict in symboli form or numerical form.

random_seed - int, default=0:

The random state fixed for classifers in scikit-learn.

verbose - int, default=0:

log print. Whether to print progress or other messages to stdout.

__init__(method='fABBA', ws=1, step=10, classifier_name='MLPClassifier', form='numeric', random_seed=0, verbose=1)

Methods

__init__([method, ws, step, ...])
construct_train(series)	Construct features and target labels for symbols.
encode(string)	Construct features and target labels for symbols and encode to numerical values.
forecast(x, y[, step, inversehash, centers])
init_classifier()
params_secure()
predict(**params)
set_symbols(series, **kwargs)	Transform time series to specified symplic representation