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: […]
How to Tune LSTM Hyperparameters with Keras for Time Series Forecasting
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 […]
How to Seed State for LSTMs for Time Series Forecasting in Python
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 […]
Time Series Forecasting with the Long Short-Term Memory Network in Python
The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations. It seems a perfect match for time series forecasting, and in fact, it may be. In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem. After completing this […]
Seasonal Persistence Forecasting With Python
It is common to use persistence or naive forecasts as a first-cut forecast on time series problems. A better first-cut forecast on time series data with a seasonal component is to persist the observation for the same time in the previous season. This is called seasonal persistence. In this tutorial, you will discover how to […]
Simple Time Series Forecasting Models to Test So That You Don’t Fool Yourself
It is important to establish a strong baseline of performance on a time series forecasting problem and to not fool yourself into thinking that sophisticated methods are skillful, when in fact they are not. This requires that you evaluate a suite of standard naive, or simple, time series forecasting models to get an idea of […]