This book is for developers that know some applied machine learning and some deep learning. The term discrete is used for series of this type even when the measured variable is continuous variable.
I have carefully designed a suite of tutorials to address these specific questions. The Promise of Deep Learning for Time Series Forecasting Traditionally, time series forecasting has been dominated by linear methods because they are well understood and effective on many simpler forecasting problems.
Clear, Complete End-to-End Examples. Recurrent neural networks directly add support for input sequence data. Methods such single, double and triple exponential smoothing also called ETS and how to grid search their hyperparameters. The goal is to approximate the real underlying mapping so well that when you have new input data, you can predict the output variables for that data.
This crash course will take you from a developer that knows a little machine learning to a developer who can bring deep learning methods to your own time series forecasting project. I assume that you are familiar with these introductory topics.
This is not a beginners book.
Neural networks do not make strong assumptions about the mapping function and readily learn linear and nonlinear relationships.
Any statistical software package ought to provide the analytical capabilities needed for the various topics covered here. Can be transformed into samples with input and output components that can be used as part of a training set to train a supervised learning model like a deep learning neural network.
A single series of observations over time.