Machine Learning

Feature Selection for Time Series Forecasting with Python

Last Updated on September 16, 2020 The use of machine learning methods on time series data requires feature engineering. A univariate time series dataset is only comprised of a sequence of observations. These must be transformed into input and output features in order to use supervised learning algorithms. The problem is that there is little limit to the type and number of features you can engineer for a time series problem. Classical time series analysis tools like the correlogram can […]

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Machine Learning

Simple Time Series Forecasting Models to Test So That You Don’t Fool Yourself

Last Updated on August 28, 2019 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 the worst acceptable performance on the problem for more sophisticated models to beat. Applying these simple models can also […]

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Machine Learning

How to Tune ARIMA Parameters in Python

Last Updated on August 21, 2019 There are many parameters to consider when configuring an ARIMA model with Statsmodels in Python. In this tutorial, we take a look at a few key parameters (other than the order parameter) that you may be curious about. Specifically, after completing this tutorial, you will know: How to suppress noisy output from the underlying mathematical libraries when fitting an ARIMA model. The effect of enabling or disabling a trend term in your ARIMA model. […]

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Machine Learning

Seasonal Persistence Forecasting With Python

Last Updated on August 28, 2019 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 implement seasonal persistence for time series forecasting in Python. After completing this tutorial, you will know: How to use […]

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Machine Learning

Time Series Forecasting with the Long Short-Term Memory Network in Python

Last Updated on August 28, 2020 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 tutorial, you will know: How to develop a baseline of performance for a forecast problem. How to design a […]

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Machine Learning

How to Seed State for LSTMs for Time Series Forecasting in Python

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 […]

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Machine Learning

How to Tune LSTM Hyperparameters with Keras for Time Series Forecasting

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 […]

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Machine Learning

How to Update LSTM Networks During Training for Time Series Forecasting

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 […]

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Machine Learning

How to Use Timesteps in LSTM Networks for Time Series Forecasting

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 […]

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Machine Learning

How to Use Features in LSTM Networks for Time Series Forecasting

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 […]

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