Название: Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Автор: Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan
Издательство: Apress
Год: 2023
Страниц: 188
Язык: английский
Формат: pdf (true), epub
Размер: 15.4 MB

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.

Before reading this book, you should have a basic knowledge of statistics, machine learning, and Python programming. If you want to learn how to build basic to advanced time series forecasting models, then this book will help by providing recipes for implementation in Python. By the end of the book, you will have practical knowledge of all the different types of modeling methods in time series.

After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python.

This book is divided into five chapters. Chapter 1 covers recipes for reading and processing the time series data and basic Exploratory Data Analysis (EDA). The following three chapters cover various forecasting modeling techniques for univariate and multivariate datasets. Chapter 2 has recipes for multiple statistical univariate forecasting methods, with more advanced techniques continued in Chapter 3. Chapter 3 also covers statistical multivariate methods. Chapter 4 covers time series forecasting using Machine Learning (regression-based). Chapter 5 is on advanced time series modeling methods using Deep Learning.

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 For
Data Scientists, Machine Learning Engineers, and software developers interested in time series analysis.