Introduction

In today's data-driven economy, mastering advanced time series econometrics is a game-changer for professionals in finance, data science, economics, and beyond. Advanced Time Series Econometrics with R/Python Training Course is designed to equip learners with practical and cutting-edge skills to analyze, forecast, and interpret complex temporal data using R and Python. With the global rise of big data and real-time analytics, time series modeling, volatility forecasting, and causal inference techniques have become essential tools for informed decision-making across industries.

This course bridges rigorous theoretical econometrics with hands-on applications in R and Python, empowering participants to build dynamic models, apply high-frequency forecasting techniques, and solve real-world problems. From ARIMA and GARCH models to state-space frameworks and machine learning-based forecasting, learners will gain valuable insights and tools to lead data analytics projects in finance, academia, central banking, policy analysis, and technology sectors.

Programme Curriculum

Advanced Time Series Econometrics with R/Python Training Course

Course Objectives

Master advanced time series modeling techniques using R and Python.
Understand and apply ARIMA, SARIMA, and exponential smoothing models.
Conduct volatility modeling using ARCH/GARCH and its extensions.
Implement unit root and stationarity tests in real datasets.
Explore vector autoregression (VAR) and vector error correction models (VECM).
Apply state-space models and Kalman filters in forecasting.
Leverage machine learning algorithms for time series prediction.
Perform seasonality and trend decomposition using STL/ETS models.
Analyze financial time series including high-frequency data.
Utilize Bayesian techniques for time series inference.
Conduct causality tests (Granger, Toda-Yamamoto) for policy analysis.
Visualize and interpret time series outputs for stakeholder reporting.
Build real-time dashboards for time series applications.

Target Audiences

Financial analysts and investment professionals
Economists and policy researchers
Data scientists and machine learning engineers
Statisticians and quantitative analysts
Academicians and graduate students in economics or finance
Central bank and regulatory staff
Business intelligence professionals
Professionals in fintech, trading, and risk management

Course Duration: 5 days

Course Modules

Module 1: Introduction to Time Series Econometrics

Overview of time series data structures
Autocorrelation and partial autocorrelation
Stationarity, trend, and seasonality
ACF/PACF interpretation with R and Python
Introduction to ARIMA models
Case Study: GDP forecasting using historical data

Module 2: Advanced ARIMA and Seasonal Modeling

ARIMA vs SARIMA modeling
Model identification and diagnostics
Automated forecasting using auto.arima and pmdarima
Cross-validation for time series models
Model comparison with AIC/BIC metrics
Case Study: Sales forecasting for a retail business

Module 3: Volatility Modeling with ARCH/GARCH

ARCH and GARCH theory and assumptions
Extensions: GJR-GARCH, EGARCH, TGARCH
Volatility clustering in financial time series
Implementation in rugarch and arch libraries
Risk metrics: VaR and conditional volatility
Case Study: Stock market volatility modeling

Module 4: Multivariate Time Series: VAR and VECM

Vector autoregression (VAR) model structure
Johansen cointegration test and VECM modeling
Impulse response functions and variance decomposition
Lag length selection and stationarity diagnostics
Forecasting with multivariate systems
Case Study: Exchange rate and interest rate analysis

Module 5: State-Space Models and Kalman Filter

Introduction to state-space frameworks
Filtering and smoothing techniques
Application of Kalman filter in dynamic systems
Time-varying parameter models
Implementation in dlm and pydlm packages
Case Study: Inflation modeling with time-varying coefficients

Module 6: Machine Learning for Time Series

Feature engineering for time series
LSTM, XGBoost, and Prophet models
Forecast accuracy and cross-validation
Hyperparameter tuning and model selection
Ensemble models for improved prediction
Case Study: Energy demand forecasting using ML

Module 7: Causal Inference and Structural Analysis

Granger causality and impulse response
Structural VARs and restrictions
Difference-in-differences (DiD) for time series
Local projection methods
Application in policy evaluation
Case Study: Fiscal policy impact on GDP

Module 8: Time Series Visualization and Dashboarding

Data visualization tools for time series
Interactive dashboards in Shiny and Dash
Real-time updating charts
Communicating forecasts to stakeholders
Exporting plots and reports
Case Study: Real-time COVID-19 tracking dashboard

Training Methodology

Hands-on coding sessions with R and Python
Real-world case studies and datasets
Group-based exercises and peer review
Live project development and feedback
Post-training assignments and certificate
Access to all scripts, templates, and recordings

Bottom of Form

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Time series modeling econometrics R/Python implementation

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000