عنوان

Time Series Algorithms Recipes :

(electronic bk.)

(electronic bk.)

9781484289785

1484289781

9781484289778

1484289773

10.1007/978-1-4842-8978-5

15130

(OCoLC)1356572959

(OCoLC)1356982902

انگلیسی

Time Series Algorithms Recipes :

Implement Machine Learning and Deep Learning Techniques with Python /

Akshay R. Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V. Adithya Krishnan.

New York, NY :

Apress L. P.,

[2023]

174p.

illustrations.

Available to OhioLINK libraries.

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.

0

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. What You Will Learn 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) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.

O'Reilly Media

9781484289785

Kulkarni, Akshay R.

Berkeley, CA : Apress L. P.,c2023

Time Series Algorithms Recipes

9781484289778.

Time-series analysis

Time-series analysis

Machine learning

Python (Computer program language)

Computer programs.

Data processing.

Computer programs.

UYQM

COM004000

UYQM

bicssc

bisacsh

thema

Kulkarni, Akshay R.,

Shivananda, Adarsha,

Kulkarni, Anoosh,

Krishnan, V. Adithya,

Ohio Library and Information Network.

کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه

20231218061624.0

rda

Implement Machine Learning and Deep Learning Techniques with Python-Apress (20.pdf

https://rave.ohiolink.edu/ebooks/ebc2/9781484289785

https://link.springer.com/10.1007/978-1-4842-8978-5

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