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Classes
TimeSeriesSplit

TimeSeriesSplit

Time Series cross-validator

Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate.

This cross-validation object is a variation of KFold. In the kth split, it returns first k folds as train set and the (k+1)th fold as test set.

Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new TimeSeriesSplit(opts?: object): TimeSeriesSplit;

Parameters

NameTypeDescription
opts?object-
opts.gap?numberNumber of samples to exclude from the end of each train set before the test set. Default Value 0
opts.max_train_size?numberMaximum size for a single training set.
opts.n_splits?numberNumber of splits. Must be at least 2. Default Value 5
opts.test_size?numberUsed to limit the size of the test set. Defaults to n\_samples // (n\_splits + 1), which is the maximum allowed value with gap=0.

Returns

TimeSeriesSplit

Defined in: generated/model_selection/TimeSeriesSplit.ts:29 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/model_selection/TimeSeriesSplit.ts:27 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/model_selection/TimeSeriesSplit.ts:26 (opens in a new tab)

_py

PythonBridge

Defined in: generated/model_selection/TimeSeriesSplit.ts:25 (opens in a new tab)

id

string

Defined in: generated/model_selection/TimeSeriesSplit.ts:22 (opens in a new tab)

opts

any

Defined in: generated/model_selection/TimeSeriesSplit.ts:23 (opens in a new tab)

Accessors

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/model_selection/TimeSeriesSplit.ts:58 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/model_selection/TimeSeriesSplit.ts:62 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/model_selection/TimeSeriesSplit.ts:115 (opens in a new tab)

get_n_splits()

Returns the number of splitting iterations in the cross-validator

Signature

get_n_splits(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?anyAlways ignored, exists for compatibility.
opts.groups?anyAlways ignored, exists for compatibility.
opts.y?anyAlways ignored, exists for compatibility.

Returns

Promise<number>

Defined in: generated/model_selection/TimeSeriesSplit.ts:132 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/model_selection/TimeSeriesSplit.ts:71 (opens in a new tab)

split()

Generate indices to split data into training and test set.

Signature

split(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data, where n\_samples is the number of samples and n\_features is the number of features.
opts.groups?ArrayLikeAlways ignored, exists for compatibility.
opts.y?ArrayLikeAlways ignored, exists for compatibility.

Returns

Promise<ArrayLike>

Defined in: generated/model_selection/TimeSeriesSplit.ts:175 (opens in a new tab)