How to Develop Deep Learning Models for Univariate Time Series Forecasting

Deep learning neural networks are capable of automatically learning and extracting features from raw data. This feature of neural networks can be used for time series forecasting problems, where models can be developed directly on the raw observations without the direct need to scale the data using normalization and standardization or to make the data stationary by differencing. Impressively, simple deep learning neural network models are capable of making skillful forecasts as compared to naive models and tuned SARIMA models on univariate time series forecasting problems that have both trend and seasonal components with no pre-processing. In this tutorial, you will discover how to develop a suite of deep learning models for univariate time series forecasting. How to Develop Deep Learning Models for Univariate Time Series Forecasting Photo by Nathaniel McQueen, some rights reserved. You can learn more about the dataset from DataMarket. Save the file with the filename'monthly-car-sales.csv' in your current working directory. We can load this dataset as a Pandas series using the function read_csv(). Once loaded, we can summarize the shape of the dataset in order to determine the number of observations. We can then create a line plot of the series to get an idea of the structure of the series. We can tie all of this together; the complete example is listed below.