AI in Time Series Prediction and Forecasting for Real-World Systems (Level 3)

Course Overview

SUMMARY:

This course provides participants with the knowledge and practical skills to analyze, model, and forecast time series data in real-world applications. Participants will learn to transform raw temporal data from energy systems, industrial processes, finance, and IoT deployments into actionable forecasts using both classical methods and advanced machine learning techniques such as LSTM and GRU networks.

Key topics include time series fundamentals, data preprocessing, feature engineering, baseline and deep learning forecasting models, model evaluation, and practical considerations like overfitting, concept drift, and operational integration. Through hands-on projects such as energy load forecasting, demand prediction, or predictive maintenance, participants will gain experience in problem formulation, model development, performance evaluation, and translating forecasts into operational decisions.

By the end of the course, participants will be able to implement end-to-end forecasting workflows, compare model performance, interpret results, and apply forecasts effectively to support planning, resource allocation, and risk management in diverse sectors.

This course is specifically designed to provide participants with:

• The ability to explore and preprocess time series data from real systems such as energy, finance, and industrial processes
• Skills to design and implement both baseline and machine-learning forecasting models for real-world prediction tasks
• Knowledge to construct relevant features and input windows to capture temporal patterns in data
• Hands-on experience to train and evaluate LSTM and GRU models for accurate short-term and multi-step forecasts
• The capability to interpret forecasting results, compare model performance, and communicate limitations and risks to support decision-making
• Practical skills to plan and integrate forecasting models into operational workflows for planning, alerts, and control

Course Curriculum

Learning Outcomes

Upon completion of this course, you will be able to:

• Understand different types of time series data and forecasting problems
• Identify and analyze trends, seasonality, cycles, and noise in time series
• Preprocess and clean time series data from real-world systems
• Implement baseline forecasting methods including naive, persistence, and moving average models
• Apply classical methods like AR/ARIMA for time series forecasting
• Engineer temporal features including lag features, sliding windows, and calendar features
• Build and train LSTM and GRU models for time series forecasting
• Implement both one-step and multi-step forecasting strategies
• Evaluate forecasting models using metrics like MAE, RMSE, and MAPE
• Handle practical challenges like overfitting, concept drift, and model maintenance
• Integrate forecasting models into operational workflows for decision support
• Apply forecasting techniques to energy, finance, industrial processes, and IoT applications

Technologies and Libraries You'll Learn

• Python - Core programming language for time series analysis
• Pandas - Data manipulation and time series handling
• NumPy - Numerical computing for time series operations
• Statsmodels - Statistical models including ARIMA
• Scikit-learn - Machine learning models for forecasting
• TensorFlow/Keras - Deep learning frameworks for LSTM and GRU
• PyTorch - Alternative deep learning framework
• Matplotlib/Seaborn - Visualization of time series and forecasts
• Jupyter Notebook - Interactive development environment

Who Should Take This Course?

• Data scientists and ML engineers working with temporal data
• Analysts in energy, finance, or industrial sectors needing forecasting capabilities
• Software developers building predictive systems for IoT and real-time applications
• Business analysts and planners requiring demand forecasting and resource allocation tools
• Anyone wanting to understand how to predict future trends from historical data

Key Benefits

• Comprehensive Coverage - From time series fundamentals to advanced LSTM/GRU models
• Real-World Applications - Learn to forecast energy demand, financial trends, and industrial processes
• Hands-On Projects - Build forecasting models through practical exercises and case studies
• Industry-Relevant Skills - Techniques applicable across energy, finance, manufacturing, and IoT sectors
• Both Classical and Modern Methods - Master both traditional ARIMA and cutting-edge deep learning approaches
• Operational Integration - Learn to deploy forecasting models in production workflows

Prerequisites

This course is designed for participants with prior knowledge of Python programming and basic machine learning concepts. Familiarity with NumPy, Pandas, and basic deep learning (neural networks) is recommended. Experience with data preprocessing and model training would be helpful. The course builds upon foundational ML knowledge and focuses specifically on time series forecasting applications.

Certification

Upon successful completion of this course, you will receive a certificate from MASET (Malaysian Association of Skills Training). This certificate validates your practical skills in time series forecasting and AI applications, demonstrating your ability to build forecasting models, analyze temporal data, handle real-world forecasting challenges, and deploy predictive systems.

The course includes hands-on projects and case studies to ensure you have practical experience with forecasting pipelines, preparing you for advanced roles in predictive analytics across various industries including energy, finance, manufacturing, and IoT.