Deep Learning Forecasting with Time Series Analysis
Specialised micro-credential for strong technical and theoretical foundation in forecasting, risk assessment, and AI-driven decision-making. Earn a Specialist Certificate and stack it towards a degree in Applied Computing.
About Course
This specialised micro-credential equips learners with essential industry-relevant skills in forecasting, risk assessment, and AI-driven decision-making.
Learners 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 modeling, correlation analysis, and risk management, addressing real-world challenges.
By leveraging advanced architectures to enhance predictive accuracy, feature extraction, and synthetic data generation, learners will gain competencies in automation, predictive analytics, and intelligent decision-making. These skills will drive innovation and improve efficiency, accuracy, and risk management across industries such as healthcare, finance, security, and AI-driven services.
Integrating online and in-person delivery, the micro-credential combines hands-on learning activities, where you will design and implement DNN, CNN, RNN, Transformer, GAN, and Autoencoder models for classification, sequential data analysis, and data manipulation. You will complete an assignment on time series modeling, participate in a case study on risk management, develop a forecasting project using RNN, LSTM, and Transformer architectures, and present a trading strategy simulation based on time series analysis.
This micro-credential is part of the CSM Pathway in Applied Computing.
Skills you’ll gain
AI and Machine Learning
Data Science and Analytics
Programming and Software Development
Competency-based Education
Who Should Attend
Data scientists
Machine Learning engineers
Individuals aiming to stack towards SIT's Bachelor of Science (Honours) in Applied Computing
Assumed Prior Knowledge
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.
Learning Outcomes
This micro-credential is predominantly delivered through a competency-based education (CBE) approach where learners acquire and demonstrate mastery of knowledge and skills that are directly relevant to job functions. This prepares them to be industry-ready where they can apply their newly acquired competencies to their work.
List of Competency Units
| Code | Competency Unit Title | Credits |
|---|---|---|
| ICT3504C | Deep Learning Architecture | 9 |
| ICT3505C | Modern Time Series Analysis | 9 |
The above are competency units that constitute this micro-credential. Upon completion of the micro-credential, you will be able to:
- Demonstrate proficiency in time series data preprocessing and apply statistical and machine learning techniques to analyse trends
- Develop real-world solutions, including venue prediction models and risk assessment tools
- Design, develop, and deploy AI-based models for trends forecasting, risk management, and decision-making
Coaching for Success
During the course, you will have access to a team of qualified success coaches who can work with you on learning strategies or to develop a personalised learning plan. Through the success coaches, you can gain access to a wide range of resources and support services, and be empowered with the necessary tools to navigate your learning journey successfully.
Teaching Team
Priyanka Bhoyar
Lecturer, Computer Science, DigiPen Institute of Technology Singapore
View profile
Wang Zhiyuan
Assistant Professor, DigiPen (Singapore)
View profile
Course Details
Schedule
| Week | Competency Unit | Learning Activity | Delivery and Time |
|---|---|---|---|
| 1 | Micro-credential and competency unit briefing | In-person 6:00 pm – 7:00 pm | |
| 1 – 13 | Online content and video lessons Self-paced online laboratory sessions Self-directed learning | Asynchronous Online | |
| 2 – 6 & 9 – 13 | ICT3504C | Consultations | In-person & Online Every Wednesday 6:00 pm – 7:00 pm |
| ICT3505C | In-person & Online Every Tuesday 6:00 pm – 7:00 pm | ||
| 3 – 5 & 10 – 12 | Assignments | Online | |
| 6 & 13 | Quizzes | Synchronous Online |
Certificate and Assessment
A Specialist Certificate in Deep Learning Forecasting with Time Series Analysis will be issued to learners who:
- Attend at least 75% of the course and
- Undertake and pass all credit bearing assessments
Assessment Plan
The learner will undertake a combination of quizzes, a case study, and a project during the course.
Fee Structure
The full fee for this course is S$10,006.20.
Note:
- A one-time, non-refundable matriculation fee of $54.50 will be collected before course commencement.
- The fee above includes GST. GST applies to individuals and Singapore-registered companies.
For Organisations
Interested in this course for your business or team?
Tailored to your organisation's specific training needs, we can help you design and develop custom courses to grow key talent and build specialist expertise to meet the future demands of work.
Course Runs
New Engineering Micro-credentials Launching Soon!
Exciting news! We are introducing new micro-credentials in Electrical and Electronic Engineering & Infrastructure and Systems Engineering. Be among the first to know by registering your interest today! Register now →