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31.5 MB
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11F872433F2413347063E2EA6EB1F643B2B883D3
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April 20, 2026, 10:02 p.m.
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(Last updated: April 20, 2026, 10:07 p.m.)
| File | Size |
|---|---|
| Vishwas B. Time Series Forecasting Using Generative AI. Leveraging AI...2025.pdf | 14.5 MB |
| Vishwas B. Hands-on Time Series Analysis with Python. From Basics...2020.pdf | 17.0 MB |
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SOURCE: Vishwas B. Time Series Forecasting Using Generative AI. Leveraging AI...2025
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COVER
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MEDIAINFO
Textbook in PDF format "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. We were looking for a resource that would equip us with the theoretical understanding of the models and practical implementation with Python sample code. We could not find any, so that gave birth to the idea of writing this book. We present this book that is catered to the needs of working professionals to come up to speed. Those who wish to dive deeper may want to read the reference papers after reading this book. - 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. Introduction Time Series Meets Generative AI Neural Networks for Time Series Transformers for Time Series Time-LLM: Reprogramming Large Language Model Chronos: Pre-trained Probabilistic Time Series Model TimeGPT: The First Foundation Model for Time Series MOIRAI: A Time Series LLM for Universal Forecasting TimesFM: Time Series Forecasting Using Decoder-Only Foundation Model Conclusion
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