The data for your sequence prediction problem probably needs to be scaled when training a neural network, such as a Long Short-Term Memory recurrent neural network. When a network is fit on unscaled data that has a range of values (e.g. quantities in the 10s to 100s) it is possible for large inputs to slow […]

# Search results for "MinMaxScaler"

## Multistep Time Series Forecasting with LSTMs in Python

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. A difficulty with LSTMs is that they […]

## Weight Regularization with LSTM Networks for Time Series Forecasting

Long Short-Term Memory (LSTM) models are a recurrent neural network capable of learning sequences of observations. This may make them a network well suited to time series forecasting. An issue with LSTMs is that they can easily overfit training data, reducing their predictive skill. Weight regularization is a technique for imposing constraints (such as L1 […]

## Dropout with LSTM Networks for Time Series Forecasting

Long Short-Term Memory (LSTM) models are a type of recurrent neural network capable of learning sequences of observations. This may make them a network well suited to time series forecasting. An issue with LSTMs is that they can easily overfit training data, reducing their predictive skill. Dropout is a regularization method where input and recurrent […]

## How to Configure Multilayer Perceptron Network for Time Series Forecasting

It can be difficult when starting out on a new predictive modeling project with neural networks. There is so much to configure, and no clear idea where to start. It is important to be systematic. You can break bad assumptions and quickly hone in on configurations that work and areas for further investigation likely to […]

## Instability of Online Learning for Stateful LSTM for Time Series Forecasting

Some neural network configurations can result in an unstable model. This can make them hard to characterize and compare to other model configurations on the same problem using descriptive statistics. One good example of a seemingly unstable model is the use of online learning (a batch size of 1) for a stateful Long Short-Term Memory […]

## Stateful and Stateless LSTM for Time Series Forecasting with Python

The Keras Python deep learning library supports both stateful and stateless Long Short-Term Memory (LSTM) networks. When using stateful LSTM networks, we have fine-grained control over when the internal state of the LSTM network is reset. Therefore, it is important to understand different ways of managing this internal state when fitting and making predictions with […]

## How to Use Features in LSTM Networks for Time Series Forecasting

The Long Short-Term Memory (LSTM) network in Keras supports multiple input features. This raises the question as to whether lag observations for a univariate time series can be used as features for an LSTM and whether or not this improves forecast performance. In this tutorial, we will investigate the use of lag observations as features […]

## How to Use Timesteps in LSTM Networks for Time Series Forecasting

The Long Short-Term Memory (LSTM) network in Keras supports time steps. This raises the question as to whether lag observations for a univariate time series can be used as time steps for an LSTM and whether or not this improves forecast performance. In this tutorial, we will investigate the use of lag observations as time […]

## How to Update LSTM Networks During Training for Time Series Forecasting

A benefit of using neural network models for time series forecasting is that the weights can be updated as new data becomes available. In this tutorial, you will discover how you can update a Long Short-Term Memory (LSTM) recurrent neural network with new data for time series forecasting. After completing this tutorial, you will know: […]