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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:
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.
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.
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:
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.
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.
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 free CodeCamp.
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 MODELSREAD 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 LEARNINGREAD 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 SCALEREAD 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
APPENDIXAPPENDIX A: INSTALLATION INSTRUCTIONS
Details
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
ISBN-13: | 9781617299889 |
ISBN-10: | 161729988X |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Peixeiro, Marco |
Hersteller: | Manning Publications |
Verantwortliche Person für die EU: | preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de |
Maße: | 231 x 189 x 27 mm |
Von/Mit: | Marco Peixeiro |
Erscheinungsdatum: | 04.10.2022 |
Gewicht: | 0,8 kg |
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 free CodeCamp.
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 MODELSREAD 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 LEARNINGREAD 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 SCALEREAD 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
APPENDIXAPPENDIX A: INSTALLATION INSTRUCTIONS
Details
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
ISBN-13: | 9781617299889 |
ISBN-10: | 161729988X |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Peixeiro, Marco |
Hersteller: | Manning Publications |
Verantwortliche Person für die EU: | preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de |
Maße: | 231 x 189 x 27 mm |
Von/Mit: | Marco Peixeiro |
Erscheinungsdatum: | 04.10.2022 |
Gewicht: | 0,8 kg |
Sicherheitshinweis