Time Series Analysis with AI Course

Module 1: Introduction to Time Series

• Time series concepts and industry applications.
• Differences from traditional datasets.

Module 2: Fundamentals of Time Series Data

• Trends, seasonality, and noise.
• Univariate vs. multivariate data.
• Data cleaning and preprocessing.

Module 3: Exploratory Data Analysis (EDA)

• Visualization techniques (line plots, ACF/PACF).
• Stationarity and data transformations.

Module 4: Classical Models

• ARIMA, SARIMA, and Holt-Winters models.
• Limitations of classical models.

Module 5: Machine Learning Approaches

• Feature extraction and supervised learning for forecasting.
• Random Forest, XGBoost, and LightGBM.

Module 6: Deep Learning Models

• Sequential modeling with RNNs, LSTMs, and GRUs.
• CNNs for time series forecasting.

Module 7: Advanced Architectures

• Temporal Convolutional Networks (TCNs).
• Transformer models and attention mechanisms.

Module 8: Anomaly Detection

• Autoencoders, Isolation Forests, and One-Class SVM.
• Applications in cybersecurity, finance, and healthcare.

Module 9: Multivariate Time Series

• VAR models and multivariate deep learning pipelines.
• Cross-correlation and dimensionality reduction.

Module 10: Probabilistic Forecasting

• Bayesian methods and Monte Carlo simulation.
• Confidence intervals and uncertainty quantification.

Module 11: Real-Time Forecasting

• Streaming data frameworks (Kafka, Spark Streaming).
• Use cases in IoT and energy grids.