Time Series Forecasting in Python

Auteur

Marco Peixeiro 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 free CodeCamp.

Texte du rabat

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.

Résumé
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
  DESCRIPTION *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.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code.
  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

Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fieldsfrom tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts.

about the book

Time Series Forecasting in Python* teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.

Contenu

table of contents detailed TOCPART 1: TIME WAITS FOR NO ONEREAD IN LIVEBOOK1UNDERSTANDING TIME SERIES FORECASTINGREAD IN LIVEBOOK2A NAVE PREDICTION OF THE FUTUREREAD IN LIVEBOOK3GOING ON A RANDOM WALKPART 2: FORECASTING WITH STATISTICAL MODELSREAD IN LIVEBOOK4MODELING A MOVING AVERAGE PROCESSREAD IN LIVEBOOK5MODELING AN AUTOREGRESSIVE PROCESSREAD IN LIVEBOOK6MODELING COMPLEX TIME SERIESREAD IN LIVEBOOK7FORECASTING NON-STATIONARY TIME SERIESREAD IN LIVEBOOK8ACCOUNTING FOR SEASONALITYREAD IN LIVEBOOK9ADDING EXTERNAL VARIABLES TO OUR MODELREAD IN LIVEBOOK10FORECASTING MULTIPLE TIME SERIESREAD IN LIVEBOOK11CAPSTONE: FORECASTING THE NUMBER OF ANTIDIABETIC DRUG PRESCRIPTIONS IN AUSTRALIAPART 3: LARGE-SCALE FORECASTING WITH DEEP LEARNINGREAD IN LIVEBOOK12INTRODUCING DEEP LEARNING FOR TIME SERIES FORECASTINGREAD IN LIVEBOOK13DATA WINDOWING AND CREATING BASELINES FOR DEEP LEARNINGREAD IN LIVEBOOK14BABY STEPS WITH DEEP LEARNINGREAD IN LIVEBOOK15REMEMBERING THE PAST WITH LSTMREAD IN LIVEBOOK16FILTERING OUR TIME SERIES WITH CNNREAD IN LIVEBOOK17USING PREDICTIONS TO MAKE MORE PREDICTIONSREAD IN LIVEBOOK18CAPSTONE: FORECASTING THE ELECTRIC POWER CONSUMPTION OF A HOUSEHOLDPART 4: AUTOMATING FORECASTING AT SCALEREAD IN LIVEBOOK19AUTOMATING TIME SERIES FORECASTING WITH PROPHETREAD IN LIVEBOOK20CAPSTONE: FORECASTING THE MONTHLY AVERAGE RETAIL PRICE OF STEAK IN CANADA21 GOING ABOVE AND BEYONDAPPENDIXAPPENDIX A: INSTALLATION INSTRUCTIONS