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Time Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting
"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies."
The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools.
Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs.
This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights.
● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.
● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.
● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.
● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.
● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.
Who this book is
Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.
The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools.
Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs.
This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights.
● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.
● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.
● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.
● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.
● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.
Who this book is
Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.
231 pages, Paperback
Published March 25, 2025
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Displaying 1 - 1 of 1 review
July 27, 2025
A Comprehensive Guide to Next-Gen Time Series Forecasting — Practical, Technical, and Forward-Looking
This book fills a much-needed gap between classical forecasting techniques and the emerging frontier of generative AI-based models.
What stands out:
1. Bridging Old and New Paradigms:
The authors don’t dismiss traditional methods like ARIMA, SARIMA, and Prophet. Instead, they place them in context — explaining their mathematical foundations, strengths (e.g., interpretability, handling seasonality), and limitations (e.g., poor scalability, inability to handle high-dimensional or irregular data).
2. Generative AI for Time Series:
Where the book shines is in explaining why and how generative models (like GPT variants) are game-changers for time series. The authors break down:
o Transformer architectures for sequential data: How self-attention mechanisms outperform RNNs/LSTMs for long-range dependencies.
o Tokenization of continuous time-series values: A clever adaptation from NLP to numerical forecasting (as seen in models like Chronos).
o Multi-task modeling: Using foundation models (like MOIRAI) for forecasting, anomaly detection, and imputation in a single pipeline.
3. Hands-On, Not Just Theory:
The book includes practical workflows for applying models like TimeGPT, Chronos, and other transformer-based solutions to real-world datasets. Code snippets, experiment setups, and parameter tuning strategies make it highly usable for practitioners.
4. Use Case Depth:
This is not just a technical manual — it shows application depth in supply chain demand planning, IT infrastructure load forecasting, financial risk modeling, energy load balancing, and anomaly detection. Each domain example helps the reader connect the dots between abstract models and real-world business problems.
5. Forward-Looking Perspective:
Rather than stopping at current implementations, the authors speculate (in a grounded way) about the future of foundation models in forecasting — integrating multimodal data, reinforcement learning for dynamic environments, and the role of synthetic data generation for improving forecasting in sparse datasets.
Who should read this:
• Data scientists & ML engineers seeking to go beyond Prophet and classical models.
• Researchers exploring generative AI’s role in sequential modeling.
• Decision-makers in operations, finance, and infrastructure who need to understand why these new models matter for their domains
This book fills a much-needed gap between classical forecasting techniques and the emerging frontier of generative AI-based models.
What stands out:
1. Bridging Old and New Paradigms:
The authors don’t dismiss traditional methods like ARIMA, SARIMA, and Prophet. Instead, they place them in context — explaining their mathematical foundations, strengths (e.g., interpretability, handling seasonality), and limitations (e.g., poor scalability, inability to handle high-dimensional or irregular data).
2. Generative AI for Time Series:
Where the book shines is in explaining why and how generative models (like GPT variants) are game-changers for time series. The authors break down:
o Transformer architectures for sequential data: How self-attention mechanisms outperform RNNs/LSTMs for long-range dependencies.
o Tokenization of continuous time-series values: A clever adaptation from NLP to numerical forecasting (as seen in models like Chronos).
o Multi-task modeling: Using foundation models (like MOIRAI) for forecasting, anomaly detection, and imputation in a single pipeline.
3. Hands-On, Not Just Theory:
The book includes practical workflows for applying models like TimeGPT, Chronos, and other transformer-based solutions to real-world datasets. Code snippets, experiment setups, and parameter tuning strategies make it highly usable for practitioners.
4. Use Case Depth:
This is not just a technical manual — it shows application depth in supply chain demand planning, IT infrastructure load forecasting, financial risk modeling, energy load balancing, and anomaly detection. Each domain example helps the reader connect the dots between abstract models and real-world business problems.
5. Forward-Looking Perspective:
Rather than stopping at current implementations, the authors speculate (in a grounded way) about the future of foundation models in forecasting — integrating multimodal data, reinforcement learning for dynamic environments, and the role of synthetic data generation for improving forecasting in sparse datasets.
Who should read this:
• Data scientists & ML engineers seeking to go beyond Prophet and classical models.
• Researchers exploring generative AI’s role in sequential modeling.
• Decision-makers in operations, finance, and infrastructure who need to understand why these new models matter for their domains
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