What is multi-step LSTM?
The Long Short-Term Memory network or LSTM is a recurrent neural network that can learn and forecast long sequences. A benefit of LSTMs in addition to learning long sequences is that they can learn to make a one-shot multi-step forecast which may be useful for time series forecasting.
What is bidirectional LSTM model?
Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence.
What is multi-step forecasting?
Multistep-ahead prediction is the task of predicting a sequence of values in a time series. A typical approach, known as multi-stage prediction, is to apply a predictive model step-by-step and use the predicted value of the current time step to determine its value in the next time step.
How to use features in LSTM networks for time series?
A representation with a 1 input feature would be the default representation when using a stateful LSTM. Using 2 to 5 features is contrived. The hope would be that the additional context from the lagged observations may improve performance of the predictive model.
How to improve multi state LSTMs for categorical features?
To further improve on this Multi-state LSTM, a next step would be to take into account the correlations between multiple labels. In fact, when predicting the performance of a student on a given pair of similar exercises, the predicted probabilities should be very similar.
How to pass multiple inputs to LSTM using TensorFlow?
The inputs will be time series of past performance data of the application, CPU usage data of the server where application is hosted, the Memory usage data, network bandwidth usage etc. I’m trying to build a solution using LSTM which will take these input data and predict the performance of the application for next one week.
Which is the default representation in a LSTM?
Let’s dive into the experiments. We will perform 5 experiments; each will use a different number of lag observations as features from 1 to 5. A representation with a 1 input feature would be the default representation when using a stateful LSTM.