Time Series Analysis with AI

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Mentor Based

Program Syllabus

Module 1

About This Course

The program focuses on AI-based approaches such as deep learning, LSTM, ARIMA, and machine learning techniques for analyzing time-dependent data. It covers methods for predicting trends, detecting anomalies, and applying real-time forecasting using AI.

Module 2

Aim

To provide advanced skills in analyzing and forecasting time series data using AI techniques. This course will guide participants through AI-powered tools to model, predict, and optimize time series data for industries like finance, healthcare, and manufacturing.

Module 3

Program Objectives

Learn fundamental techniques in time series analysis.
Apply AI models like LSTM and RNN for time series forecasting.
Understand how to detect anomalies using AI.
Gain proficiency in multivariate time series predictions.
Build real-time AI forecasting models for various industries.

Module 4

Program Structure

Introduction to Time Series Analysis
- Overview of Time Series Data
- Time Series vs. Traditional Data
- Applications of Time Series in Finance, Healthcare, and IoT
Fundamentals of Time Series Data
- Components of Time Series: Trend, Seasonality, and Noise
- Types of Time Series: Univariate and Multivariate
- Data Preprocessing for Time Series: Handling Missing Data, Smoothing, and Transformation
Exploratory Data Analysis (EDA) for Time Series
- Visualization Techniques for Time Series Data
- Statistical Methods for Time Series EDA
- Stationarity and Differencing
Classical Time Series Models
- Moving Average (MA), Autoregressive (AR), and ARIMA Models
- Seasonal Decomposition of Time Series (STL)
- Exponential Smoothing (Holt-Winters)
Machine Learning for Time Series Forecasting
- Feature Engineering for Time Series
- Regression Techniques for Time Series Forecasting
- Random Forests and Gradient Boosting for Time Series
Deep Learning for Time Series
- Recurrent Neural Networks (RNNs) for Sequential Data
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
- Using CNNs for Time Series Forecasting
Advanced Time Series Techniques with AI
- Temporal Convolutional Networks (TCN)
- Transformers for Time Series Forecasting
- Sequence-to-Sequence Models
Time Series Anomaly Detection with AI
- Introduction to Anomaly Detection in Time Series
- AI Techniques for Anomaly Detection (Autoencoders, Isolation Forest)
- Applications: Fraud Detection, Network Intrusion Detection
Multivariate Time Series Analysis
- Working with Multiple Time Series Variables
- Vector Autoregression (VAR)
- AI Techniques for Multivariate Time Series Prediction
Probabilistic Forecasting and Uncertainty in Time Series
- Bayesian Methods for Time Series Forecasting
- Quantifying Uncertainty in AI Models
- Applications in Demand Forecasting, Weather Forecasting
Real-Time Time Series Analysis
- Real-Time Data Streams and Online Learning
- AI for Real-Time Forecasting and Decision Making
- Use Cases: Real-Time Stock Market Analysis, IoT Monitoring

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.