Foundation Models for Time Series Analysis and Semantic Search

Overview

Explore the forefront of foundation models in time-series analysis with this comprehensive course. Gain insights into cutting-edge neural architectures designed for time-sensitive data and enhance your predictive analytics skills.

Integrate semantic search capabilities with Milvus and learn to construct RAG and agentic systems, optimized for temporal data management. Ideal for those keen on advancing their knowledge in artificial intelligence and computer science.

Syllabus

• Introduction to Foundation Models
Overview of foundation models
Importance in time-series analysis and semantic search
Historical development and recent advancements
• Time-Series Analysis with Foundation Models
Fundamental concepts in time-series data
Traditional vs. foundation models for time-series
Specialized neural architectures for time-series analysis
Case studies of foundation models in time-series forecasting
• Neural Architectures for Time-Series
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
Temporal Convolutional Networks (TCNs)
Transformers for time-series
Attention mechanisms
• Predictive Capabilities in Time-Series
Building predictive models
Accuracy and evaluation metrics
Handling seasonality and trends
Anomaly detection
• Semantic Search with Milvus
Introduction to semantic search
Key features of Milvus
Implementing Milvus for semantic search
Integrating Milvus with time-series analysis
• RAG Systems for Temporal Data
Understanding Retrieval-Augmented Generation (RAG)
Designing RAG systems for time-series
Data retrieval and context augmentation
Applications and use cases
• Agentic Systems for Temporal Data
Concept of agentic systems
Building agentic models for time-series data
Reinforcement learning approaches
Real-world applications
• Integration and Implementation
System design and architecture
Data pre-processing techniques
Model deployment and monitoring
Scalability and optimization
• Case Studies and Applications
Industry applications of foundation models
Case studies in finance, healthcare, and IoT
Future trends and research directions
• Course Summary and Next Steps
Review of key concepts and techniques
Resources for further learning
Capstone project or practical assignment
Course feedback and closing remarks

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Subjects

Computer Science