Time Series Forecasting in Python

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Beschreibung

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.

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

Über den Autor

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 freeCodeCamp.

Inhaltsverzeichnis

table of contents detailed TOC

PART 1: TIME WAITS FOR NO ONE

READ IN LIVEBOOK1UNDERSTANDING TIME SERIES FORECASTING

READ IN LIVEBOOK2A NAï¿¿VE PREDICTION OF THE FUTURE

READ IN LIVEBOOK3GOING ON A RANDOM WALK

PART 2: FORECASTING WITH STATISTICAL MODELS

READ IN LIVEBOOK4MODELING A MOVING AVERAGE PROCESS

READ IN LIVEBOOK5MODELING AN AUTOREGRESSIVE PROCESS

READ IN LIVEBOOK6MODELING COMPLEX TIME SERIES

READ IN LIVEBOOK7FORECASTING NON-STATIONARY TIME SERIES

READ IN LIVEBOOK8ACCOUNTING FOR SEASONALITY

READ IN LIVEBOOK9ADDING EXTERNAL VARIABLES TO OUR MODEL

READ IN LIVEBOOK10FORECASTING MULTIPLE TIME SERIES

READ IN LIVEBOOK11CAPSTONE: FORECASTING THE NUMBER OF ANTIDIABETIC DRUG PRESCRIPTIONS IN AUSTRALIA

PART 3: LARGE-SCALE FORECASTING WITH DEEP LEARNING

READ IN LIVEBOOK12INTRODUCING DEEP LEARNING FOR TIME SERIES FORECASTING

READ IN LIVEBOOK13DATA WINDOWING AND CREATING BASELINES FOR DEEP LEARNING

READ IN LIVEBOOK14BABY STEPS WITH DEEP LEARNING

READ IN LIVEBOOK15REMEMBERING THE PAST WITH LSTM

READ IN LIVEBOOK16FILTERING OUR TIME SERIES WITH CNN

READ IN LIVEBOOK17USING PREDICTIONS TO MAKE MORE PREDICTIONS

READ IN LIVEBOOK18CAPSTONE: FORECASTING THE ELECTRIC POWER CONSUMPTION OF A HOUSEHOLD

PART 4: AUTOMATING FORECASTING AT SCALE

READ IN LIVEBOOK19AUTOMATING TIME SERIES FORECASTING WITH PROPHET

READ IN LIVEBOOK20CAPSTONE: FORECASTING THE MONTHLY AVERAGE RETAIL PRICE OF STEAK IN CANADA

21 GOING ABOVE AND BEYOND

APPENDIX

APPENDIX A: INSTALLATION INSTRUCTIONS

Details

Erscheinungsjahr:	2022
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Seiten:	456
Inhalt:	Kartoniert / Broschiert
ISBN-13:	9781617299889
ISBN-10:	161729988X
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Peixeiro, Marco
Hersteller:	Manning Publications
Maße:	231 x 189 x 27 mm
Von/Mit:	Marco Peixeiro
Erscheinungsdatum:	10.11.2022
Gewicht:	0,8 kg

preigu-id: 121138882