Time Series Analysis with AI

Aim

Program Objectives

Program Structure

1. Introduction to Time Series Analysis
   • Overview of Time Series Data
   • Time Series vs. Traditional Data
   • Applications of Time Series in Finance, Healthcare, and IoT
2. 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
3. Exploratory Data Analysis (EDA) for Time Series
   • Visualization Techniques for Time Series Data
   • Statistical Methods for Time Series EDA
   • Stationarity and Differencing
4. Classical Time Series Models
   • Moving Average (MA), Autoregressive (AR), and ARIMA Models
   • Seasonal Decomposition of Time Series (STL)
   • Exponential Smoothing (Holt-Winters)
5. 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
6. 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
7. Advanced Time Series Techniques with AI
   • Temporal Convolutional Networks (TCN)
   • Transformers for Time Series Forecasting
   • Sequence-to-Sequence Models
8. 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
9. Multivariate Time Series Analysis
   • Working with Multiple Time Series Variables
   • Vector Autoregression (VAR)
   • AI Techniques for Multivariate Time Series Prediction
10. Probabilistic Forecasting and Uncertainty in Time Series
    • Bayesian Methods for Time Series Forecasting
    • Quantifying Uncertainty in AI Models
    • Applications in Demand Forecasting, Weather Forecasting
11. 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

Who Should Enrol?

Program Outcomes

Fee Structure

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