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## Can a LSTM be used in a different time series?

The input time-series can be of different length when LSTM is used (even the batch sizes can be different from one batch to another, but obvisouly the dimension of features should be the same). Here is an example in Keras:

**How to create a variable length input LSTM in keras?**

I am trying to do some vanilla pattern recognition with an LSTM using Keras to predict the next element in a sequence. where the label of the training sequence is the last element in the list: X_train [‘Sequence’] [n] [-1].

**Can a masking layer be used on a LSTM network?**

I am training a LSTM network on variable-length inputs using a masking layer but it seems that it doesn’t have any effect. Input shape (100, 362, 24) with 362 being the maximum sequence lenght, 24 the number of features and 100 the number of samples (divided 75 train / 25 valid).

### Which is the first axis of the LSTM layer?

As you can see the first and second axis of Input layer is None. It means they are not pre-specified and can be any value. You can think of LSTM as a loop. No matter the input length, as long as there are remaining data vectors of same length (i.e. n_feats ), the LSTM layer processes them.

**When to concatenate time series with LSTMs?**

Consider, for example, a continuous series from day 1 to day 10 and another continuous series from day 15 to day 20. Simply concatenating them to a single series might yield wrong results.

**How to train LSTMs with time series in keras?**

Consider, for example, a continuous series from day 1 to day 10 and another continuous series from day 15 to day 20. Simply concatenating them to a single series might yield wrong results. I see basically two options to bring them to shape (batch_size, timesteps, output_features):

## Is the number of timesteps in LSTM networks a limiting factor?

There is a general trend of increasing test RMSE as the number of time steps is increased. The expectation of increased performance with the increase of time steps was not observed, at least with the dataset and LSTM configuration used. This raises the question as to whether the capacity of the network is a limiting factor.

**What do you need to know about LSTM in Python?**

When you implement LSTM, you should be very clear of what are the features and what are the element you want the model to read each time step. There is a very similar case here surely can help you.

**When does RMSE increase in a LSTM network?**

The average test RMSE appears lowest when the number of neurons and the number of time steps is set to one. A box and whisker plot is created to compare the distributions. The trend in spread and median performance almost shows a linear increase in test RMSE as the number of neurons and time steps is increased.

### How to use different sequence lengths in the same network?

For example if you have a sequence of length 300 and one of length 500, to use them as input in the same network, you will have to either extend the length of the smaller sequence by padding the ends of it some how, or break up the longer one into smaller sub-sequences.

**How to handle very long sequences with LSTMs?**

Explore splitting the input sequence into multiple fixed-length subsequences and train a model with each subsequence as a separate feature (e.g. parallel input sequences). Explore a Bidirectional LSTM where each LSTM in the pair is fit on half of the input sequence and the outcomes of each layer are merged.

**How to use LSTM for regression with time steps?**

LSTM for Regression with Time Steps. You may have noticed that the data preparation for the LSTM network includes time steps. Some sequence problems may have a varied number of time steps per sample. For example, you may have measurements of a physical machine leading up to a point of failure or a point of surge.