LSTM

LSTM

LSTM (Long Short-Term Memory)
DescriptionForecasting and Prediction of the model include numerical and date dataset.
Why to useForecasting Time series
When to useTo classify, process, and make predictions based on time series data.When not to useOn textual data and categorical data.
Prerequisites
  • The independent variable should be of interval type.
  • The dependent variable should be of numerical type. 
Input

Any dataset that contains time interval as well as numerical type of data.

OutputThe predicted value of the dependent variable.
Statistical Methods used
  • Mean Squared Error
  • Mean Absolute Error
Limitations
  • LSTMs cannot solve the problem of vanishing gradients.
  • LSTMs take a lot of time to get trained for complex data.
  • LSTMs are prone to overfitting.

LSTM is a model that can be used for solving Univariate and Multivariate time series forecasting problems. LSTM is used to learn from the series of past observations to predict the next value in the sequence. It has the ability to learn the context required to make predictions, rather than having this context pre-specified and fixed.

With LSTM, the user can select multidimensional functionality for the target variable specifically. The multidimensional functionality allows the user to predict the accuracy or predict the model’s accuracy for multiple dimensions. LSTM is a technique that employs data models and uses statistical tools to predict outcomes. Although LSTM cannot perform future analysis with 100% accuracy, it can predict the possible outcome.

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