Random Forests are flexible and powerful when it comes to tabular data. Do they also work for time-series forecasting? Let's find out.
One primary limitation of GARCH is the restriction to a single dimensional time-series. In reality, however, we are typically dealing with multiple time-series.
You have heard about integrated time-series data but what about cointegration?
Facebook Prophet is highly popular for time-series forecasting. Let me show you why I am not a big fan and what else you can use.
CUSUM is arguably the simplest algorithm for change point detection problems as in IoT or finance applications.
A quick Jupyter notebook about LSTMs and Copulas using tensorflow probability.
Who says that the GARCH conditional distribution needs to be Gaussian?
Yet another ARMA time-series model for non-normal data.
Bayesian inference with less pain and even less code.
Personal thoughts about a somewhat controversial paradigm.
How Gaussian Process regression can handle varying variance.