English | 2021 | ISBN: ‎ 1801819629 | 371 pages | True (PDF EPUB) | 29.43 MB

Become proficient in deriving insights from -series data and analyzing a model's performance
Key Features
Explore popular and modern machine learning methods including the latest online and deep learning algorithms
Learn to increase the accuracy of your predictions by matching the right model with the right problem
Master -series via real-world case studies on operations management, digital marketing, finance, and healthcare
Book Description
Machine learning has emerged as a powerful tool to understand hidden complexities in -series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences.

These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.
This book covers Python basics for -series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading -series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature eeering.
Machine Learning for -Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.
By the end of this book, you will be proficient in effectively analyzing -series datasets with machine learning principles.
What you will learn
Understand the main classes of -series and learn how to detect outliers and patterns
Choose the right method to solve -series problems
Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
Get to grips with -series data visualization
Understand classical -series models like ARMA and ARIMA
Implement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning models
Become familiar with many libraries like prophet, xgboost, and TensorFlow
Who This Book Is For
This book is ideal for data analysts, data scientists, and Python developers who are looking to perform -series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.
Table of Contents
Introduction to Series with Python
-Series Analysis with Python
Preprocessing -Series
Machine Learning for -Series
-Series Forecasting with Moving Averages and Autoregressive Models
Unsupervised Methods for Series
Machine Learning Models for Series
Online Learning for Series
Probabilistic Models
Deep Learning for Series
Reinforcement Learning for -Series
Case Studies



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