AI for Time Series: Volume 2 by Min Wu (.PDF)
File Size: 52.0 MB
AI for Time Series: Volume 2: Building Robust and Generalizable Models by Min Wu, Emadeldeen Eldele, Zhenghua Chen, Shirui Pan, Qingsong Wen, Xiaoli Li
Requirements: .PDF reader, 52.0 MB | True PDF
Overview: With the rapid advancement of Deep Learning, time series analysis has moved beyond handcrafted statistical models toward data-driven architectures capable of discovering high-dimensional temporal structures automatically. However, despite the remarkable success of models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers, time series data remain uniquely difficult to model. Time series are typically non-stationary, multi-scale, and heterogeneous across domains, often leading to substantial performance degradation when data distributions shift over time or across environments. Consequently, the community has begun to explore a new generation of AI models for time series that not only achieve accuracy under ideal conditions but also maintain robustness and generalizability across tasks and domains. This shift motivates the development of large-scale, transferable, and foundation-level models that can encode temporal intelligence in a unified manner.
Genre: Non-Fiction > Tech & Devices
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