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Time Series Forecasting in Python

Om Time Series Forecasting in Python

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 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.

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  • Språk:
  • Okänt
  • ISBN:
  • 9781617299889
  • Format:
  • Häftad
  • Sidor:
  • 456
  • Utgiven:
  • 10. november 2022
  • Mått:
  • 189x27x231 mm.
  • Vikt:
  • 800 g.
  I lager
Leveranstid: 4-7 vardagar
Förväntad leverans: 4. december 2024

Beskrivning av Time Series Forecasting in Python

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 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.

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