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 Recognize a time series forecasting problem and build a performant predictive modelCreate univariate forecasting models that account for seasonal effects and external variablesBuild multivariate forecasting models to predict many time series at onceLeverage large datasets by using deep learning for forecasting time seriesAutomate the forecasting processTime 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.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyYou 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 bookTime 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.What's insideCreate models for seasonal effects and external variablesMultivariate forecasting models to predict multiple time seriesDeep learning for large datasetsAutomate the forecasting processAbout the readerFor data scientists familiar with Python and TensorFlow.About the authorMarco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks.Table of ContentsPART 1 TIME WAITS FOR NO ONE1 Understanding time series forecasting2 A naive prediction of the future3 Going on a random walkPART 2 FORECASTING WITH STATISTICAL MODELS4 Modeling a moving average process5 Modeling an autoregressive process6 Modeling complex time series7 Forecasting non-stationary time series8 Accounting for seasonality9 Adding external variables to our model10 Forecasting multiple time series11 Forecasting the number of antidiabetic drug prescriptions in AustraliaPART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING12 Introducing deep learning for time series forecasting13 Data windowing and creating baselines for deep learning14 Baby steps with deep learning15 Remembering the past with LSTM16 Filtering a time series with CNN17 Using predictions to make more predictions18 Forecasting the electric power consumption of a householdPART 4 AUTOMATING FORECASTING AT SCALE19 Automating time series forecasting with Prophet20 Forecasting the monthly average retail price of steak in Canada21 Going above and beyond