Weight Regularization with LSTM Networks for Time Series Forecasting

Last Updated on August 28, 2020 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 or L2) on the weights within LSTM nodes. This has the effect of reducing overfitting and improving model performance. […]

September 29, 2020 Machine Learning

How to Convert a Time Series to a Supervised Learning Problem in Python

Last Updated on August 21, 2019 Machine learning methods like deep learning can be used for time series forecasting. Before machine learning can be used, time series forecasting problems must be re-framed as supervised learning problems. From a sequence to pairs of input and output sequences. In this tutorial, you will discover how to transform univariate and multivariate time series forecasting problems into supervised learning problems for use with machine learning algorithms. After completing this tutorial, you will know: How […]

September 29, 2020 Machine Learning

Multistep Time Series Forecasting with LSTMs in Python

Last Updated on August 28, 2020 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 can be tricky to configure and it can require a lot of preparation to get the data in the […]

September 29, 2020 Machine Learning

Demonstration of Memory with a Long Short-Term Memory Network in Python

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning over long sequences. This differentiates them from regular multilayer neural networks that do not have memory and can only learn a mapping between input and output patterns. It is important to understand the capabilities of complex neural networks like LSTMs on small contrived problems as this understanding will help you scale the network up to large and even very […]

September 29, 2020 Machine Learning

How to use Different Batch Sizes when Training and Predicting with LSTMs

Last Updated on August 14, 2019 Keras uses fast symbolic mathematical libraries as a backend, such as TensorFlow and Theano. A downside of using these libraries is that the shape and size of your data must be defined once up front and held constant regardless of whether you are training your network or making predictions. On sequence prediction problems, it may be desirable to use a large batch size when training the network and a batch size of 1 when […]

September 29, 2020 Machine Learning

How to Use the TimeDistributed Layer in Keras

Last Updated on August 14, 2019 Long Short-Term Networks or LSTMs are a popular and powerful type of Recurrent Neural Network, or RNN. They can be quite difficult to configure and apply to arbitrary sequence prediction problems, even with well defined and “easy to use” interfaces like those provided in the Keras deep learning library in Python. One reason for this difficulty in Keras is the use of the TimeDistributed wrapper layer and the need for some LSTM layers to […]

September 29, 2020 Machine Learning

Learn to Add Numbers with an Encoder-Decoder LSTM Recurrent Neural Network

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) that are capable of learning the relationships between elements in an input sequence. A good demonstration of LSTMs is to learn how to combine multiple terms together using a mathematical operation like a sum and outputting the result of the calculation. A common mistake made by beginners is to simply learn the mapping function from input term to the output term. […]

September 29, 2020 Machine Learning

The Promise of Recurrent Neural Networks for Time Series Forecasting

Last Updated on August 5, 2019 Recurrent neural networks are a type of neural network that add the explicit handling of order in input observations. This capability suggests that the promise of recurrent neural networks is to learn the temporal context of input sequences in order to make better predictions. That is, that the suite of lagged observations required to make a prediction no longer must be diagnosed and specified as in traditional time series forecasting, or even forecasting with […]

September 29, 2020 Machine Learning

A Gentle Introduction to Long Short-Term Memory Networks by the Experts

Last Updated on February 20, 2020 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. This is a behavior required in complex problem domains like machine translation, speech recognition, and more. LSTMs are a complex area of deep learning. It can be hard to get your hands around what LSTMs are, and how terms like bidirectional and sequence-to-sequence relate to the field. In this post, you will get […]

September 29, 2020 Machine Learning

On the Suitability of Long Short-Term Memory Networks for Time Series Forecasting

Last Updated on August 5, 2019 Long Short-Term Memory (LSTM) is a type of recurrent neural network that can learn the order dependence between items in a sequence. LSTMs have the promise of being able to learn the context required to make predictions in time series forecasting problems, rather than having this context pre-specified and fixed. Given the promise, there is some doubt as to whether LSTMs are appropriate for time series forecasting. In this post, we will look at […]

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