Modern Time Series Forecasting Techniques For Predictive Analytics and Anomaly Detection: From Classical Foundations to Cutting-Edge Applications

Buy the Paperback – Get the Complimentary Digital Edition Purchase the paperback edition and enjoy a complimentary digital edition on your favorite device—yours to keep for personal use. The print edition adopts the classic Springer font and layout for pleasing reading. The digital edition adopts a beautiful Latex layout for pleasing reading. WHO THIS BOOK IS FOR This book is an ideal textbook for an upper-level undergraduate course in time series analysis, or for any career changers to time series. WHAT THIS BOOK COVERS This book organizes time series models and applications into six meticulously crafted parts. Each part equips you with the knowledge and skills to conduct time series forecasting and anomaly detection. The six parts are: Part 1: From Prophet to NeuralProphet Part 2: Getting Probabilistic Forecasts Part 3: Autoregressive-based Time Series Techniques Part 4: Tree-based Time Series Techniques Part 5: Deep into Deep learning-based Time Series Techniques Part 6: Transformer-based Time Series Techniques WHY READ THIS BOOK? In this book, you will find a wide coverage of methodologies, algorithms, and applications. Whether you’re a seasoned data scientist seeking to refine your expertise or a novice eager to embark on your analytical odyssey, this book offers a roadmap tailored to cater to diverse skill levels and objectives. You will emerge equipped with the proficiency and confidence to unravel intricate temporal patterns, harness predictive power, and unlock new horizons of insight across a myriad of domains, from finance and economics to healthcare and beyond. The writing style of this book is another selling point. Instead of taking a technique as given, this book first describes intuitions and then dives into detail. This book also gives a landscaping view from one idea to the consequent ideas. With the real-world data cases in this book, you will gain a deeper understanding of how time series techniques are applied in diverse domains. TABLE OF CONTENTS Preface Introduction Prophet for business forecasting Tutorial I Tutorial II Change Point Detection in Time Series Monte Carlo Simulation for Probabilistic Forecasting Quantile Regression for Probabilistic Forecasting Conformal Predictions for Probabilistic Forecasting Conformalized Quantile Regression for Probabilistic Forecasting Automatic ARIMA! Time Series Data Formats Made Easy Linear Regression for Multi-period Probabilistic Forecasting Feature Engineering for Tree-based Time Series Models Two Primary Strategies for Multi-period Time Series Forecasting Tree-based XGB, LightGBM, and CatBoost Models for Multi-period Probabilistic Forecasting The Progression of Time Series Modeling Techniques Deep Learning-based DeepAR for Probabilistic Forecasting Application — Probabilistic Predictions for stock prices From RNN to Transformer-based Time Series Models Temporal Fusion Transformer for Interpretable Time Series Predictions Lag-Llama for Time Series Forecasting WHAT YOU GET IN THE BOOK Learning time series techniques comprehensively in a short period of time A roadmap from the classical techniques to modern time series forecasting Applying forecasting for resource planning and anomaly detection Mastering time series Python libraries Model interpretability Model evaluation metrics Hands-on example code Cheat Sheets