Deep Learning Forecasting with Time Series Analysis (Synchronous & Asynchronous e-Learning)
About This Course
The Deep Learning Forecasting with Time Series Analysis micro-credential equips learners with essential industry-relevant skills in forecasting, risk assessment, and Artificial Intelligence driven (AI-driven) decision-making. Participants will apply statistical and machine learning models to analyse time series data, identify trends, and optimise decision-making strategies. They will develop expertise in volatility modelling, correlation analysis, and risk management, addressing real-world challenges.
The module also integrates deep learning architectures to enhance predictive accuracy, feature extraction, and synthetic data generation for practical applications. Deep learning competencies will enhance automation, predictive analytics, and intelligent decision-making across various industries. These advancements will improve efficiency, accuracy, and innovation in fields such as healthcare, finance, security, and AI-driven services.
Overall, this micro-credential provides a comprehensive understanding of modern time series analysis and deep learning techniques, equipping professionals with the skills needed for data-driven insights and effective risk management.
What You'll Learn
• Demonstrate proficiency in time series data preprocessing and apply statistical and machine learning techniques to analyse trends.
• Develop real-world solutions, including value prediction models and risk assessment tools.
• Design, develop, and deploy Artificial Intelligence based (AI-based) models for trend forecasting, risk management, and decision-making.
• Develop Deep Neural Networks (DNNs) for diverse data-driven applications and implement Convolutional Neural Networks (CNNs) for image classification and feature extraction.
• Apply Recurrent Neural Networks (RNNs) for time-dependent data modelling and leverage Transformer architectures for sequence processing.
• Develop Generative Adversarial Networks (GANs) for generating high-quality synthetic data and Autoencoders for data encoding and feature extraction.
• Apply advanced statistical techniques - Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA) - to model time series data, interpret trends, and enhance forecasting accuracy.
• Utilise volatility modelling, correlation analysis, and co-movement evaluation to quantify and mitigate risks in diverse scenarios.
• Implement RNN, Long Short-Term Memory (LSTM), and Transformer architectures to develop predictive models for value prediction, decision-making strategies, and risk management.
• Formulate and implement strategies for value prediction, decision-making strategies development, and risk modelling.
Entry Requirements
It is recommended that learners have a basic understanding of Python programming, as well as foundational knowledge in probability and statistics, linear algebra, and calculus.