Time Series Analysis

Pattern Recognition Chronicles

Learning Outcome

Hook/Story/Analogy(Slide 4)

Transition from Analogy to Technical Concept(Slide 5)

Introduction to Pattern Recognition

• Process of identifying patterns in data

• Uses algorithms and statistics

• Core part of AI systems

• Used in vision, speech, and predictions

Why Pattern Recognition Matters

Automates decision making

Enables predictive analytics

Used in classification & clustering

Helps detect anomalies

Replaces manual sorting and human error in large datasets

Forecasts future trends based on historical patterns

Organizes data into meaningful groups and classes

Identifies rare events or outliers like fraud

Real-world Examples

Face recognition

Unlocking phones & security systems.

Difitizing notes & postal sorting

Forecasting market trends

Identifying anomalies in X-rays.

Handwritten text recognition

Stock prediction

Disease detection

Key Components

Each component is essential for accurate pattern recognition results

Types of Pattern Recognition

Supervised Learning

Learns from labeled training data with known outcomes

Unsupervised Learning

Finds patterns in unlabeled data without guidance

Semi-supervised Learning

Combines small labeled data with large unlabeled data

Reinforcement Learning

Learns through trial and error with reward feedback

Feature Extraction

Why It Matters

Extract meaningful information from data

Helps distinguish patterns

Most important step

Examples:

Image : edges, colors

Text : word frequency

Audio : pitch

Time series : mean, variance

Classification Algorithms

Pattern Recognition in Time Series

Shape-based matching

Feature-based modeling

Deep learning (RNN, LSTM)

Use case: stock price prediction

Challenges

Summary

Which step is MOST important for identifying meaningful patterns?

Which step is MOST important for identifying meaningful patterns?