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. Time Series Forecasting with the Long Short-Term Memory Network in Python Photo by Matt MacGillivray, some rights reserved. This is a big topic and we are going to cover a lot of ground. This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this tutorial.

We can see that on average this model configuration achieved a test RMSE of about 92 monthly shampoo sales with a standard deviation of 5. A box and whisker plot is also created from the distribution of test RMSE results and saved to a file. The plot provides a clear depiction of the spread of the results, highlighting the middle 50% of values (the box) and the median (green line).

Notably, this includes the mean and standard deviations of the RMSE scores from each population of results. The mean gives an idea of the average expected performance of a configuration, whereas the standard deviation gives an idea of the variance. The min and max RMSE scores also give an idea of the range of possible best and worst case examples that might be expected. Looking at just the mean RMSE scores, the results suggest that an epoch configured to 1000 may be better. The results also suggest further investigations may be warranted of epoch values between 1000 and 2000.

In this tutorial, you discovered how to establish a baseline performance on time series forecast problems with Python. The importance of establishing a baseline and the persistence algorithm that you can use. How to implement the persistence algorithm in Python from scratch. How to evaluate the forecasts of the persistence algorithm and use them as a baseline. The importance of establishing a baseline and the persistence algorithm that you can use. How to implement the persistence algorithm in Python from scratch. How to evaluate the forecasts of the persistence algorithm and use them as a baseline. Do you have any questions about baseline performance, or about this tutorial? Ask your questions in the comments below and I will do my best to answer.