{"product_id":"time-series-forecasting-in-python-9781617299889","title":"Time Series Forecasting in Python","description":"\u003cb\u003eBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eTime Series Forecasting in Python\u003c\/i\u003e you will learn how to: \u003cp\u003e\u003c\/p\u003e Recognize a time series forecasting problem and build a performant predictive model\u003cbr\u003e Create univariate forecasting models that account for seasonal effects and external variables\u003cbr\u003e Build multivariate forecasting models to predict many time series at once\u003cbr\u003e Leverage large datasets by using deep learning for forecasting time series\u003cbr\u003e Automate the forecasting process \u003cp\u003e\u003c\/p\u003e \u003ci\u003eTime Series Forecasting in Python\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e About the technology\u003cbr\u003e 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. \u003cp\u003e\u003c\/p\u003e About the book\u003cbr\u003e \u003ci\u003eTime Series Forecasting in Python\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e What's inside \u003cp\u003e\u003c\/p\u003e Create models for seasonal effects and external variables\u003cbr\u003e Multivariate forecasting models to predict multiple time series\u003cbr\u003e Deep learning for large datasets\u003cbr\u003e Automate the forecasting process \u003cp\u003e\u003c\/p\u003eAbout the reader\u003cbr\u003e For data scientists familiar with Python and TensorFlow. \u003cp\u003e\u003c\/p\u003e About the author\u003cbr\u003e \u003cb\u003eMarco Peixeiro\u003c\/b\u003e is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks. \u003cp\u003e\u003c\/p\u003eTable of Contents\u003cbr\u003e PART 1 TIME WAITS FOR NO ONE\u003cbr\u003e 1 Understanding time series forecasting\u003cbr\u003e 2 A naive prediction of the future\u003cbr\u003e 3 Going on a random walk\u003cbr\u003e PART 2 FORECASTING WITH STATISTICAL MODELS\u003cbr\u003e 4 Modeling a moving average process\u003cbr\u003e 5 Modeling an autoregressive process\u003cbr\u003e 6 Modeling complex time series\u003cbr\u003e 7 Forecasting non-stationary time series\u003cbr\u003e 8 Accounting for seasonality\u003cbr\u003e 9 Adding external variables to our model\u003cbr\u003e 10 Forecasting multiple time series\u003cbr\u003e 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia\u003cbr\u003e PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING\u003cbr\u003e 12 Introducing deep learning for time series forecasting\u003cbr\u003e 13 Data windowing and creating baselines for deep learning\u003cbr\u003e 14 Baby steps with deep learning\u003cbr\u003e 15 Remembering the past with LSTM\u003cbr\u003e 16 Filtering a time series with CNN\u003cbr\u003e 17 Using predictions to make more predictions\u003cbr\u003e 18 Capstone: Forecasting the electric power consumption of a household\u003cbr\u003e PART 4 AUTOMATING FORECASTING AT SCALE\u003cbr\u003e 19 Automating time series forecasting with Prophet\u003cbr\u003e 20 Capstone: Forecasting the monthly average retail price of steak in Canada\u003cbr\u003e 21 Going above and beyond\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eMarco Peixeiro\u003c\/b\u003e is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks. He is an active contributor to Towards Data Science, an instructor on Udemy, and on YouTube in collaboration with freeCodeCamp.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":50508137660690,"sku":"9781617299889","price":55.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_05915b28-0562-4377-9015-cc9b989d3b79.jpg?v=1730873402","url":"https:\/\/surprise-castle.myshopify.com\/products\/time-series-forecasting-in-python-9781617299889","provider":"Surprise Castle","version":"1.0","type":"link"}