Time Series Forecasting in Python

  • 10h 53m 47s
  • Marco Peixeiro
  • Manning Publications
  • 2022

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.

In Time Series Forecasting in Python you will learn how to:

  • Recognize a time series forecasting problem and build a performant predictive model
  • Create univariate forecasting models that account for seasonal effects and external variables
  • Build multivariate forecasting models to predict many time series at once
  • Leverage large datasets by using deep learning for forecasting time series
  • Automate the forecasting process

Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.

about the technology

You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before.

about the book

Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you’ll soon be ready to build your own accurate, insightful forecasts.

About the Author

Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks.

In this Audiobook

  • Chapter 1 - Understanding time series forecasting
  • Chapter 2 - A naive prediction of the future
  • Chapter 3 - Going on a random walk
  • Chapter 4 - Modeling a moving average process
  • Chapter 5 - Modeling an autoregressive process
  • Chapter 6 - Modeling complex time series
  • Chapter 7 - Forecasting non-stationary time series
  • Chapter 8 - Accounting for seasonality
  • Chapter 9 - Adding external variables to our model
  • Chapter 10 - Forecasting multiple time series
  • Chapter 11 - Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
  • Chapter 12 - Introducing deep learning for time series forecasting
  • Chapter 13 - Data windowing and creating baselines for deep learning
  • Chapter 14 - Baby steps with deep learning
  • Chapter 15 - Remembering the past with LSTM
  • Chapter 16 - Filtering a time series with CNN
  • Chapter 17 - Using predictions to make more predictions
  • Chapter 18 - Capstone: Forecasting the electric power consumption of a household
  • Chapter 19 - Automating time series forecasting with Prophet
  • Chapter 20 - Capstone: Forecasting the monthly average retail price of steak in Canada
  • Chapter 21 - Going above and beyond
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