Foundation Models for Time Series Analysis and Semantic Search

Overview

Explore cutting-edge foundation models for time-series analysis, including specialized neural architectures, predictive capabilities, semantic search with Milvus, and building RAG and agentic systems for temporal data.

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

Subjects

Computer Science