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AI in Time Series Prediction and Forecasting for Real-World Systems (Level 3)
This applied course provides participants with the knowledge and tools to build predictive models for time-dependent data. It introduces the fundamentals of time series analysis, including trend, seasonality, and noise, and progresses to modern forecasting methods using regression, ARIMA concepts, and deep learning architectures such as LSTM and GRU. Participants will gain hands-on experience in data preparation, feature engineering, and model evaluation using real datasets from energy, industrial, and financial domains. The course emphasizes practical implementation and interpretation of forecasting results, helping participants integrate predictive models into planning, control, or maintenance workflows. By the end of the course, participants will be able to design, train, and evaluate forecasting models that support data-driven decisions across various real-world systems.
Open to graduates and working professionals from engineering, data science, computer science, finance, or related fields who wish to develop or strengthen their skills in predictive analytics and machine learning. Suitable for engineers, analysts, and researchers handling time-dependent data such as sensor readings, financial metrics, or operational logs, as well as individuals seeking to specialize in AI-based forecasting and data modeling.
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Method
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Duration
4 Days
Inovasi Edutech M Sdn Bhd
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