### Forecasting with Decision Trees and Random Forests

Time Series

Decision Trees

Today, Deep Learning dominates many areas of modern machine learning. On the other hand, Decision Tree based models still shine particularly for tabular data. If you look up the winning solutions of respective Kaggle challenges, chances are high that a tree…

### Cointegrated time-series and when differencing might be bad

Time Series

A standard method in the time-series analysis toolkit are difference transformations or

*differencing*. Despite being dead simple, differencing can be quite powerful. In fact, it allows us to outperform sophisticated time-series models with what is almost a bare white…### Probabilistic CUSUM for change point detection

Time Series

Change Point Detection

According to the famous principle of [Occam’s Razor], simpler models are more likely to…

### Let’s make GARCH more flexible with Normalizing Flows

Time Series

For financial time-series data, GARCH (Generalized AutoRegressive Conditional Heteroscedasticity) models play an important role. While forecasting mean returns is usually futile, stock volatility appears to be predictable, at least to some extent. However, standard…

### A Gaussian Process model for heteroscedasticity

Bayesian

Gaussian Processes

A common phenomenon when working on continuous regression problems is the non-constant residual variance, also known as heteroscedasticity. While heteroscedasticity is often seen in Statistics and Econometrics, it doesn’t seem to receive as much attention in mainstream Machine Learning and Data…

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