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Time series algorithms recipes = imp...

Kulkarni, Akshay R.

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Time series algorithms recipes = implement machine learning and deep learning techniques with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time series algorithms recipes/ by Akshay R Kulkarni ... [et al.].
Reminder of title:	implement machine learning and deep learning techniques with Python /
Author:	Kulkarni, Akshay R.
other author:	Shivananda, Adarsha.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xvi, 174 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Getting Started with Time Series -- Chapter 2: Statistical Univariate Modelling -- Chapter 3: Statistical Multivariate Modelling -- Chapter 4: Machine Learning Regression-Based Forecasting -- Chapter 5: Forecasting Using Deep Learning.
Contained By:	Springer Nature eBook
Subject:	Time-series analysis - Computer programs. -
Online resource:	https://doi.org/10.1007/978-1-4842-8978-5
ISBN:	9781484289785

Time series algorithms recipes = implement machine learning and deep learning techniques with Python /
Kulkarni, Akshay R.

Time series algorithms recipesimplement machine learning and deep learning techniques with Python /[electronic resource] :by Akshay R Kulkarni ... [et al.]. - Berkeley, CA :Apress :2023. - xvi, 174 p. :ill., digital ;24 cm.

Chapter 1: Getting Started with Time Series -- Chapter 2: Statistical Univariate Modelling -- Chapter 3: Statistical Multivariate Modelling -- Chapter 4: Machine Learning Regression-Based Forecasting -- Chapter 5: Forecasting Using Deep Learning.

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average) Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. You will: Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory)

ISBN: 9781484289785

Standard No.: 10.1007/978-1-4842-8978-5doiSubjects--Topical Terms:

784594
Time-series analysis
--Computer programs.

LC Class. No.: HA30.3 / .K85 2023

Dewey Class. No.: 006.31

Time series algorithms recipes = implement machine learning and deep learning techniques with Python /
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245 1 0 $a Time series algorithms recipes $h [electronic resource] : $b implement machine learning and deep learning techniques with Python / $c by Akshay R Kulkarni ... [et al.].
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505 0 $a Chapter 1: Getting Started with Time Series -- Chapter 2: Statistical Univariate Modelling -- Chapter 3: Statistical Multivariate Modelling -- Chapter 4: Machine Learning Regression-Based Forecasting -- Chapter 5: Forecasting Using Deep Learning.
520 $a This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average) Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. You will: Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory)
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