Contents
What are LSTM models good for?
LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. LSTMs were developed to deal with the vanishing gradient problem that can be encountered when training traditional RNNs.
How does LSTM model improve accuracy?
More layers can be better but also harder to train. As a general rule of thumb — 1 hidden layer work with simple problems, like this, and two are enough to find reasonably complex features. In our case, adding a second layer only improves the accuracy by ~0.2% (0.9807 vs. 0.9819) after 10 epochs.
Is LSTM good for prediction?
Introduction. LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.
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 develop test harness to systematically evaluate LSTM time steps?
How to develop a test harness to systematically evaluate LSTM time steps for time series forecasting. The impact of using a varied number of lagged observations as input time steps for LSTM models. The impact of using a varied number of lagged observations and matching numbers of neurons for LSTM models.
How are transform functions used in the LSTM model?
Transform the observations to have a specific scale. Specifically, to rescale the data to values between -1 and 1 to meet the default hyperbolic tangent activation function of the LSTM model. These transforms are inverted on forecasts to return them into their original scale before calculating and error score.
How is root mean squared error used in LSTM?
The root mean squared error (RMSE) will be used as it punishes large errors and results in a score that is in the same units as the forecast data, namely monthly shampoo sales. Before we can fit an LSTM model to the dataset, we must transform the data.