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Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
575 pages, ebook
Published January 1, 2018
About the author
Jason Brownlee
47 books71 followersJason is a software engineer and research scientist with a background in artificial intelligence and high-performance computing. He has authored more than 20 technical books on machine learning and has built, operated, and exited online businesses.
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