### Forecasting with Decision Trees and Random Forests

Random Forests are flexible and powerful when it comes to tabular data. Do they also work for time-series forecasting? Let's find out.

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.