An Introduction to Time Series
Discover how to model, interpret, and forecast time series data. Learn about stochasticity, stationarity, ARIMA models, and decomposition. Gain skills to explore, model, and forecast using Python.
- 40 lessons across 9 modules
- 6 quizzes with instant feedback
- 2 projects in preconfigured environments
- 1 AI mock interview with scoring rubrics
- AI Tutor on every lesson
Hello! My name is Álvaro, and I am a Data Scientist based in London, UK. An Economist by training, my interest in Machine Learning and AI originally stemmed from the intersection between traditional Econometrics and modern applications in Data Science, especially around causal inference and time series. My interests are now broader and include other exciting sub-fields, the last of which is NLP. My primary programming language is Python. My professional background is in consulting and sales teams, developing user-centric solutions using Agile methodologies. In my courses, I try to make my industry and academic experience as accessible as possible for everyone who wants to start a career in Data Science or skill up in their current role. I am also passionate about sharing my knowledge and skills through writing blogs. I have published a few articles on Towards Data Science on Medium. Please check them out!
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What You'll Learn
- Understanding of properties of time series, including its moments, stationarity, autocorrelation, seasonality, and trend
- Hands-on experience in analyzing and modeling real-world time series using Python’s statsmodel
- Familiarity with modeling time series as autoregressive (AR) and moving average (MA) processes and their combinations ARMA and ARIMA
- Working knowledge of point forecasting with ARIMA models using Pythons’s statsmodel
Learning Roadmap
1.
Introduction to Time Series
Introduction to Time Series
2.
The Basics of Time Series
The Basics of Time Series
3.
Exploring Data
Exploring Data
5 Lessons
5 Lessons
4.
The Properties of Time Series
The Properties of Time Series
10 Lessons
10 Lessons
5.
ARIMA Models
ARIMA Models
5 Lessons
5 Lessons
6.
On Prediction
On Prediction
4 Lessons
4 Lessons
7.
Choosing, Fitting, and Evaluating Models
Choosing, Fitting, and Evaluating Models
6 Lessons
6 Lessons
9.
Appendix
Appendix
2 Lessons
2 Lessons
Alvaro Corrales Cano
Hello! My name is Álvaro, and I am a Data Scientist. I specialise in a wide array of Econometric and Machine Learning techniques, including (but not limited to) time series, causal inference and NLP.
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