Logistics and Supply Chain Management

Time Series Forecasting with Machine Learning Training Course

Introduction

In today’s data-driven world, Time Series Forecasting with Machine Learning has become an essential skill for professionals working in fields like finance, retail, energy, and healthcare. Time Series Forecasting with Machine Learning Training Course introduces learners to advanced machine learning techniques for predictive analytics using real-world time series data. With the rise of big data and AI, being proficient in time-dependent forecasting gives businesses a competitive edge through improved decision-making and resource optimization.

This hands-on course is packed with cutting-edge algorithms, deep learning techniques, and practical forecasting strategies using Python, R, and other popular tools. Participants will learn to build, evaluate, and deploy models using ARIMA, LSTM, XGBoost, and Prophet, among others. With a strong focus on real-world applications, the course combines theory, live demonstrations, and case studies from various industries to ensure practical understanding.

Programme Curriculum

Time Series Forecasting with Machine Learning Training Course

Introduction

Course Objectives

Understand the fundamentals of time series data and its unique characteristics.
Apply statistical and machine learning techniques for trend, seasonality, and anomaly detection.
Develop autoregressive models including AR, MA, ARIMA, and SARIMA.
Master deep learning models like RNN and LSTM for sequential forecasting.
Implement Facebook Prophet for interpretable forecasting.
Utilize ensemble methods such as Random Forest and XGBoost.
Perform multivariate time series analysis.
Conduct time series cross-validation and performance evaluation.
Apply Python libraries (pandas, statsmodels, scikit-learn) and R packages for forecasting tasks.
Automate forecasting pipelines and model tuning.
Leverage cloud-based tools for scalable forecasting (AWS, GCP).
Integrate forecasting models into business dashboards and APIs.
Analyze real-world case studies across finance, retail, and healthcare.

Target Audiences

Data Scientists & Machine Learning Engineers
Business Intelligence Analysts
Financial Analysts & Planners
Supply Chain Analysts
Operations Managers
IT Professionals & Developers
Students in Data Science & AI programs
Academics & Researchers in Econometrics and Forecasting

Course Duration: 5 days

Course Modules

Module 1: Introduction to Time Series

Time series data structure & components
Exploratory data analysis (EDA) techniques
Visualizing trends, cycles, and seasonality
Stationarity & transformation methods
Data decomposition and smoothing
Case Study: Seasonal sales analysis in retail

Module 2: Classical Forecasting Methods

Moving averages and exponential smoothing
Holt-Winters method
AR, MA, ARIMA models
SARIMA for seasonal modeling
Forecasting accuracy metrics
Case Study: Forecasting electricity demand

Module 3: Machine Learning for Time Series

Time series framing for ML models
Feature engineering & lag variables
Supervised learning (Random Forest, XGBoost)
Recursive vs direct forecasting
Model evaluation and tuning
Case Study: Predicting stock prices

Module 4: Deep Learning Models

Introduction to sequence modeling
RNN and LSTM architectures
Model building using TensorFlow/Keras
Handling long sequences and overfitting
Training and validation with time windows
Case Study: Health monitoring using sensor data

Module 5: Prophet & Hybrid Models

Facebook Prophet: components and setup
Handling holidays and events
Comparison with traditional models
Building hybrid models with ARIMA + ML
Advanced model stacking techniques
Case Study: Forecasting airline passenger data

Module 6: Multivariate Time Series

Vector Autoregression (VAR) basics
Granger causality tests
Feature correlation and selection
Cointegration and differencing
Implementation in Python
Case Study: Economic indicator forecasting

Module 7: Forecasting at Scale

Deploying models on AWS/GCP/Azure
Automating data pipelines with Airflow
Using MLFlow for model management
Time series forecasting in BigQuery
Integrating with BI dashboards
Case Study: Real-time forecasting for eCommerce traffic

Module 8: Capstone Project & Evaluation

End-to-end forecasting project
Model development, validation, and deployment
Documentation and presentation skills
Peer review and feedback
Final evaluation and certification
Case Study: Custom project based on industry

Training Methodology

Instructor-led live sessions with interactive demos
Real-world case studies and hands-on labs
Project-based learning with continuous feedback
Group discussions and breakout problem-solving
Access to code notebooks, datasets, and tools

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 forecasting ML applications

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