LSTMTimeSeries
Extends:
Implements:
Long Short Term Memory Time Series with Tensorflow
Static Method Summary
Static Public Methods | ||
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createDataset(dataset: Array<Array<number>, look_back: Number): [Array<Array<number>>,Array<number>] Creates dataset data |
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getTimeseriesDataSet(timeseries: *, look_back: *) Returns data for predicting values |
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public static |
getTimeseriesShape(x_timeseries: Array<Array<number>): Array<Array<number>> Reshape input to be [samples, time steps, features] |
Constructor Summary
Public Constructor | ||
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constructor(options: {layers: Array<Object>, compile: Object, fit: Object}, properties: *) |
Member Summary
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Method Summary
Public Methods | ||
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generateLayers(x_matrix: Array<Array<number>>, y_matrix: Array<Array<number>>, layers: Array<Object>, x_test: Array<Array<number>>, y_test: Array<Array<number>>) Adds dense layers to tensorflow classification model |
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async predict() |
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async train() |
Static Public Methods
public static createDataset(dataset: Array<Array<number>, look_back: Number): [Array<Array<number>>,Array<number>] source
Creates dataset data
Params:
Name | Type | Attribute | Description |
dataset | Array<Array<number> | array of values |
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look_back | Number | number of values in each feature |
Return:
[Array<Array<number>>,Array<number>] | returns x matrix and y matrix for model trainning |
Example:
LSTMTimeSeries.createDataset([ [ 1, ], [ 2, ], [ 3, ], [ 4, ], [ 5, ], [ 6, ], [ 7, ], [ 8, ], [ 9, ], [ 10, ], ], 3) // =>
// [
// [
// [ [ 1 ], [ 2 ], [ 3 ] ],
// [ [ 2 ], [ 3 ], [ 4 ] ],
// [ [ 3 ], [ 4 ], [ 5 ] ],
// [ [ 4 ], [ 5 ], [ 6 ] ],
// [ [ 5 ], [ 6 ], [ 7 ] ],
// [ [ 6 ], [ 7 ], [ 8 ] ],
// ], //x_matrix
// [ [ 4 ], [ 5 ], [ 6 ], [ 7 ], [ 8 ], [ 9 ] ] //y_matrix
// ]
public static getTimeseriesDataSet(timeseries: *, look_back: *) source
Returns data for predicting values
Params:
Name | Type | Attribute | Description |
timeseries | * | ||
look_back | * |
public static getTimeseriesShape(x_timeseries: Array<Array<number>): Array<Array<number>> source
Reshape input to be [samples, time steps, features]
Params:
Name | Type | Attribute | Description |
x_timeseries | Array<Array<number> | dataset array of values |
Return:
Array<Array<number>> | returns proper timeseries forecasting shape |
Example:
LSTMTimeSeries.getTimeseriesShape([
[ [ 1 ], [ 2 ], [ 3 ] ],
[ [ 2 ], [ 3 ], [ 4 ] ],
[ [ 3 ], [ 4 ], [ 5 ] ],
[ [ 4 ], [ 5 ], [ 6 ] ],
[ [ 5 ], [ 6 ], [ 7 ] ],
[ [ 6 ], [ 7 ], [ 8 ] ],
]) //=> [6, 1, 3,]
Public Constructors
public constructor(options: {layers: Array<Object>, compile: Object, fit: Object}, properties: *) source
Params:
Name | Type | Attribute | Description |
options | {layers: Array<Object>, compile: Object, fit: Object} | neural network configuration and tensorflow model hyperparameters |
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properties | * | extra instance properties |
Public Members
public createDataset source
public getTimeseriesDataSet source
public getTimeseriesShape source
public layers source
public model source
public xShape source
public yShape source
Public Methods
public calculate() source
public generateLayers(x_matrix: Array<Array<number>>, y_matrix: Array<Array<number>>, layers: Array<Object>, x_test: Array<Array<number>>, y_test: Array<Array<number>>) source
Adds dense layers to tensorflow classification model
Params:
Name | Type | Attribute | Description |
x_matrix | Array<Array<number>> | independent variables |
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y_matrix | Array<Array<number>> | dependent variables |
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layers | Array<Object> | model dense layer parameters |
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x_test | Array<Array<number>> | validation data independent variables |
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y_test | Array<Array<number>> | validation data dependent variables |