LSTM

LSTM

LSTM is located under Forecasting in Modeling, in the task pane on the left. Use drag-and-drop method to use algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Refer to Properties of LSTM.


Properties of LSTM

The available properties of LSTM are as shown in the figure given below.


The table given below describes the different fields present on the properties of LSTM.

FieldDescriptionRemark
RunIt allows you to run the node.-
ExploreIt allows you to explore the successfully executed node.-
Vertical Ellipses

The available options are

  • Run till node
  • Run from node
  • Publish as a model
  • Publish code
-
Task Name

It displays the name of the selected task.

You can click the text field to edit or modify the name of the task as required.

Time Id Variable

It allows you to select the variable from the drop-down list for which we need to predict the values of the dependent variable.

  • Only one data field can be selected
  • Data fields with only date values are available.
Target Variables

It allows you to select the experimental or predictor variable(s).

  • Only one data fields can be selected.
  • Data fields with only numerical values are available.
Group byIt allows you group the values by a column
  • Only one data field can be selected
  • Only categorical data fields are available

Advanced

Time Steps

It provides feedback from the predicted value going back and forth.

It should be selected as per the data. If the target variables are more than one, time steps can be selected more.

Dimensions Of Sequence Of Networks

Number of neurons to be used in the model to learn complex pattern from the data.—
Loss Function
  • It is the error between the actual value of the target variable and the predicted value of the target variable. It is the function to be used to calculate the accuracy.

There are two types of loss functions:

  • Mean Absolute Error is a measure of errors between paired and observation expressing the same phenomenon.
  • Mean Squared Error is the difference between the estimated values and the actual value.
Optimizer
  • It calculates the learning rate for each parameter present in the model and compares it with other adaptive learning algorithms.
  • It is the function used to increase the performance of the model.
  • Optimizers shape and mold the model into its most accurate possible form with the help of weights.
Adam optimizer is an optimization algorithm that can be used instead of the Classification stochastic gradient descent procedure to update network weights iterative based on training data.
Activation Function

  • It is a mathematical equation that determines the output of a neural network.

  • It is used in a network to learn complex patterns in the data.
  • It is used for LSTM blocks.

The two values are –

  • Sigmoid – It is used for binary classification.
  • Softmax – It is used for multi-classification.
As it is a neuron based model, it decides whether to fire the next neuron or not.
Batch Size
  • It is the function that limits the number of samples to be shown to the network before the weight update can be performed.
  • It is used when fitting the model to control the number of predictions must be made at a time.
  • It sets the limits of the processed records in each batch.
  • It should be the power of two.
  • Its value depends on the sample of data, usually it is 32. 64,128 are also used while experimenting.

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