Lecture 0: Introduction

• Overview
  
• Software Requirements
  
• Data

Lecture 1.1: Manipulations, Transformations & Visualizations

Manipulations:
- summarizing by time
- padding by time
- filtering by time
- mutating by time
- joining by time

Transformations:
- variance reduction
- range reduction
- smoothing
- rolling averages
- missing values imputation
- anomaly detection and cleaning
- lags and differencing
- Fourier transforms
- confined intervals

Visualizations:
- time series
- autocorrelations
- cross-correlations
- smoothing
- seasonality
- time series decomposition
- anomaly detection
- time series regressions

Lecture 1.2: Feature Engineering

Feature Engineering:
- time-based features
- trend-based features
- seasonal features
- interaction features
- rolling average features
- lag features
- Fourier features
- event data features
- external features

Tidymodel:
- recipes
- workflows

Lecture 1.3: Modeltime

Modeltime:
- package framework
- algorithm specifications
- workflows
- calibration
- evaluation
- refitting
- forecasting

Lecture 1.4: Statistical Models

Forecasting Methods:
- Naive / Seasonal Naive
- Window Functions
- S-ARIMA-X
- Exponential Smoothing
- TBATS
- STLM (Decomposition models)
- Facebook’s Prophet

Lecture 1.5: Machine Learning Models

Forecasting Methods:
- Linear Regression
- Elastic Net
- MARS
- SVM
- KNN
- Random Forest
- Boosting
- Cubist
- Neural Network

Lecture 2.1: Deep Learning Models

Forecasting Methods:
- GluonTS Deep AR
- GluonTS NBEATS
- GluonTS GP Forecaster
- GluonTS Deep State
- Torch Deep AR

Lecture 2.2: Automatic Machine Learning

Forecasting Methods:
- H2O

Lecture 2.3: Hyperparameter Tuning

Methods:
- Sequential
- Non-Sequential

Cross Validation:
- Time Series Cross Validation
- V-Fold Cross Validation
- Grid Searches

Lecture 2.4: Ensemble Learning Methods

Forecasting Methods:
- Simple Averaging Ensemble
- Weight Averaging Ensemble
- Stacking and Metalearning
- Multi-level Ensemble

Lecture 2.5: Recursive Forecasting

Recursivity:
- single time series recursive modelling
- multiple time series recursive modelling

Lecture 3.1: Global Forecasting Method

Forecasting Methods:
- Nested Forecasting
- Global Modelling

Lecture 3.2: Global Recursive Forecasting

Recursivity:
- Global Recursive Modelling

Lecture 3.3: Foundation Models

Forecasting Methods:
- Pretrained Models
- Few-Shot Learning
- Zero-Shot Learning
- Nixtla’s TimeGPT

Lecture 3.4: Time Series Agents

Agents:
- Introduction to Agents
- Time Series Forecasting Agents
- TimeCopilot

Advanced Topics & Algorithms

• Mixed Models (ARIMA Boost and Prophet Boost)
  
• Facebook’s Neural Prophet
  
• Anomaly Detetction
  
• Clustering