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.

After completing this tutorial, you will know:

  • How to develop a robust test harness using walk-forward validation for evaluating the performance of neural network models.
  • How to develop and evaluate simple multilayer Perceptron and convolutional neural networks for time series forecasting.
  • How to develop and evaluate LSTMs, CNN-LSTMs, and ConvLSTM neural network models for time series forecasting.

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