How does a commercial bank forecast the expected performance of their loan portfolio?
Or how does an investment manager estimate a stock portfolio’s risk?
Which are the quantitative methods used to predict real-estate properties?
If there is some time dependency, then you know it – the answer is: time series analysis.
This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.
In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:
· Easy to understand
· To the point
· Packed with plenty of exercises and resources
But we know that may not be enough.
We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…
Welcome to Time Series Analysis in Python!
The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis.
We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.
Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.
With these tools we will master the most widely used models out there:
· AR (autoregressive model)
· MA (moving-average model)
· ARMA (autoregressive-moving-average model)
· ARIMA (autoregressive integrated moving average model)
· ARIMAX (autoregressive integrated moving average model with exogenous variables)
. SARIA (seasonal autoregressive moving average model)
. SARIMA (seasonal autoregressive integrated moving average model)
. SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)
· ARCH (autoregressive conditional heteroscedasticity model)
· GARCH (generalized autoregressive conditional heteroscedasticity model)
. VARMA (vector autoregressive moving average model)
We know that time series is one of those topics that always leaves some doubts.
This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.
Check: AWS Course