Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 40 Minute(s)
Language
English
Taught by
Marco Peixeiro
Rating
Course Overview
Master time series forecasting from statistical to state-of-the-art deep learning models in 100% Python code
Master Time Series Forecasting: From Fundamentals to Deep Learning
Unlock the power of predictive analytics in this comprehensive 12-hour course designed specifically for aspiring data scientists. Whether you're looking to forecast market trends, optimize supply chains, or predict weather patterns, this course will equip you with the essential skills to tackle real-world forecasting challenges.
What You'll Learn
Transform from a beginner to a confident practitioner through our carefully structured curriculum. Starting with fundamental statistical models, you'll progress to implementing cutting-edge deep learning architectures. Along the way, you'll master:
Classical forecasting methods (ARIMA, SARIMA, SARIMAX)
Advanced techniques like exponential smoothing, TBATS, and the Theta model
Deep learning architectures for time series
Facebook's Prophet framework
Why This Course Stands Out
14+ hands-on projects that reinforce your learning
100% Python-based curriculum with complete code implementations
Real-world applications across finance, economics, retail, and supply chain
Progressive learning path from basics to advanced concepts
Perfect For You If...
You're new to time series forecasting but have basic Python programming skills. No prior forecasting experience needed – we'll guide you through every step, from understanding the fundamentals to implementing advanced predictive models.
Course Structure
The curriculum flows naturally from foundational concepts to advanced applications:
Core statistical methods and their practical implementation
Multivariate forecasting techniques for complex datasets
Deep learning approaches built from the ground up
Modern frameworks and state-of-the-art architectures
About Your Instructor
Learn from an industry expert at the forefront of time series innovation. I am a contributor at Nixtla, a leader in open-source forecasting technology, and an active developer of NeuralForecast, the Python package renowned for its lightning-fast deep learning implementations. This isn't just theoretical knowledge – it's practical insight from someone who shapes the tools that industry leaders use today.
By the end of this course, you'll have the skills and confidence to tackle diverse forecasting challenges across any industry. Join us to master one of the most valuable skills in data science, backed by extensive hands-on practice and real-world applications.
Ready to predict the future? Enroll now and transform your data science journey.
Course Content
- 9 section(s)
- 57 lecture(s)
- Section 1 Introduction
- Section 2 The random walk model
- Section 3 Forecasting with the ARIMA model
- Section 4 Multivariate forecasting
- Section 5 Exponential smoothing
- Section 6 Forecasting multiple seasonal periods
- Section 7 Forecasting using decomposition
- Section 8 Deep learning for time series forecasting
- Section 9 EXTRA - Prophet
What You’ll Learn
- The basics of time series forecasting using baseline models
- Apply statistical models like ARIMA, ETS, TBATS and more
- Apply deep learning architectures for time series forecasting
- Use state-of-the-art deep learning models like NHITS, TSMixer, iTransformer, TimeGPT, and more!
Skills covered in this course
Reviews
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ZZoltán Gelencsér
Some parts are obsolete. The fbprophet doesn't work under Python 3.12.
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VVaughn V
Material seems simple so far, would’ve loved if it was more in depth. The instructor has not answered one question in the Q&A, big red flag
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JJonathan Jones
Great introduction to time series forecasting. The course provides a solid foundation into more advanced forecasting techniques with an early focus on baselines and ARIMA!
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LLuiza Bondar
This course is an excellent starter in the topic of time series analysis.
