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Why I prefer probabilistic forecasts - hitting time probabilities

Point forecasts are good for making decisions. With probabilistic forecasts, you can also make the right ones.

Random Forests and Boosting for ARCH-like volatility forecasts

Tree models are not just useful for point and mean forecasts.

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.

Multivariate GARCH with Python and Tensorflow

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.

Cointegrated time-series and when differencing might be bad

You have heard about integrated time-series data but what about cointegration?

Facebook Prophet, Covid and why I don't trust the Prophet

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.

Probabilistic CUSUM for change point detection

CUSUM is arguably the simplest algorithm for change point detection problems as in IoT or finance applications.

Multivariate, probabilistic time-series forecasting with LSTM and Gaussian Copula

A quick Jupyter notebook about LSTMs and Copulas using tensorflow probability.

Let's make GARCH more flexible with Normalizing Flows

Who says that the GARCH conditional distribution needs to be Gaussian?

ARMA forecasting for non-Gaussian time-series data using Copulas

Yet another ARMA time-series model for non-normal data.

Bayesian Machine Learning and Julia are a match made in heaven

Bayesian inference with less pain and even less code.

When is Bayesian Machine Learning actually useful?

Personal thoughts about a somewhat controversial paradigm.

A Gaussian Process model for heteroscedasticity

How Gaussian Process regression can handle varying variance.