Time Series and Machine Learning – An Overview and Book Introduction

In the field of artificial intelligence, Large Language Models (LLMs) refer to deep‑learning models with massive parameters that require huge computational resources for training and inference.

Recent advances in computing power and data availability have made large models a key trend in AI research and applications.

These models demonstrate remarkable capabilities in complex tasks, especially in natural language processing, computer vision, and speech recognition.

The era of big data provides abundant "fuel" for training such data‑intensive models, as massive amounts of text, images, and video are collected from the Internet.

Time‑series analysis, an important branch of data science, is gaining increasing importance across industries.

It is widely applied in finance, industrial optimization, healthcare monitoring, and intelligent operations support.

Time‑series data, which records observations over time, appears in many domains—from stock prices and patient vital signs to retail sales volumes.

Enterprises need to predict future trends from historical time‑series data to drive business growth.

With the dramatic reduction in data‑communication costs, sensors and smart devices continuously generate massive time‑series data that are sent to the cloud.

These large data resources enable real‑time monitoring of business or equipment status and the generation of multi‑dimensional reports.

Big‑data analytics and machine‑learning techniques can provide predictions and early warnings, helping organizations make scientific decisions, reduce costs, and create new value.

Time‑series data is ubiquitous, and effective analysis of these temporal samples is crucial for both industry and scientific research.

Examples include a supermarket manager needing to forecast weekly sales from daily product data, an operations engineer monitoring system health, and a ride‑hailing market manager predicting regional demand to allocate drivers efficiently.

Financial technology and AI have also applied deep‑learning models to predict financial time‑series such as gold prices, stocks, and futures.

Many companies and financial institutions are deploying AI models for real‑world time‑series prediction.

Traditional time‑series methods like ARIMA or seasonal decomposition, while effective in many cases, struggle with large‑scale data and complex nonlinear patterns.

Integrating machine‑learning, especially deep‑learning and large‑model techniques, into time‑series analysis has become a research hotspot.

The book "Time Series and Machine Learning" is written to fill the gap of literature that combines these two fields.

The book is organized into eight chapters: Chapter 1 – Overview of Time Series introduces fundamentals, history, applications, and connections to other fields; Chapter 2 – Feature Extraction covers feature engineering for time‑series; Chapter 3 – Forecasting discusses classic models (AR, MA, ARIMA, exponential smoothing, Prophet) and neural networks (RNN, LSTM, Transformer, Informer); Chapter 4 – Anomaly Detection presents probabilistic, reconstruction‑based, distance‑based, supervised, and weakly‑supervised methods; Chapter 5 – Similarity and Clustering explains distance measures (Euclidean, DTW) and clustering algorithms (K‑Means, DBSCAN); Chapter 6 – Multivariate Time Series explores OLAP processing, root‑cause analysis, and related techniques; Chapter 7 – Intelligent Operations describes monitoring, capacity forecasting, auto‑scaling, alarm correlation, and holiday effects; and Chapter 8 – Financial Applications covers quantitative trading, factor mining, asset pricing, allocation, and volatility prediction.

The authors, both experienced practitioners in time‑series, provide practical tools, examples, and case studies for industry professionals as well as academic insights for researchers.

Readers are encouraged to explore the book to deepen their understanding of time‑series analysis combined with machine‑learning techniques.