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
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.gap? | number | Number of samples to exclude from the end of each train set before the test set. Default Value 0 |
opts.max_train_size? | number | Maximum size for a single training set. |
opts.n_splits? | number | Number of splits. Must be at least 2. Default Value 5 |
opts.test_size? | number | Used 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
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
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Always ignored, exists for compatibility. |
opts.groups? | any | Always ignored, exists for compatibility. |
opts.y? | any | Always 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
| Name | Type |
|---|---|
py | PythonBridge |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.groups? | ArrayLike | Always ignored, exists for compatibility. |
opts.y? | ArrayLike | Always ignored, exists for compatibility. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/TimeSeriesSplit.ts:175 (opens in a new tab)