Last Updated on August 28, 2020 Long Short-Term Memory networks, or LSTMs, are a powerful type of recurrent neural network capable of learning long sequences of observations. A promise of LSTMs is that they may be effective at time series forecasting, although the method is known to be difficult to configure and use for these purposes. A key feature of LSTMs is that they maintain an internal state that can aid in the forecasting. This raises the question of how […]

Last Updated on August 28, 2020 Configuring neural networks is difficult because there is no good theory on how to do it. You must be systematic and explore different configurations both from a dynamical and an objective results point of a view to try to understand what is going on for a given predictive modeling problem. In this tutorial, you will discover how you can explore how to configure an LSTM network on a time series forecasting problem. After completing […]

Last Updated on September 6, 2020 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: How to update an LSTM neural network with new data. How to develop a test harness to evaluate different […]

Last Updated on August 28, 2020 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 steps in LSTMs models in Python. After completing this tutorial, you will know: How to develop a test harness […]

Last Updated on August 28, 2020 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 in LSTM models in Python. After completing this tutorial, you will know: How to develop a test harness to […]

Last Updated on August 28, 2020 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 LSTM networks affect the skill of the network. In this tutorial, you will explore the performance of stateful and […]

Last Updated on August 28, 2020 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 (LSTM) model. In this tutorial, you will discover how to explore the results of a stateful LSTM fit using […]

Last Updated on August 28, 2020 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 payoff. In this tutorial, you will discover how to use exploratory configuration of multilayer perceptron (MLP) neural networks to […]

Last Updated on August 28, 2020 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 connections to LSTM units are probabilistically excluded from activation and weight updates while training a network. This has the […]