Carsten Croonenbroeck

Obtaining Superior Wind Power Predictions from a Periodic and Heteroscedastic Wind Power Prediction Tool

The Wind Power Prediction Tool (WPPT) has successfully been used for accurate wind power forecasts in the short to medium term scenario (up to 12 hours ahead). Since its development about a decade ago, a lot of additional stochastic modeling has been applied to the interdependency of wind power and wind speed. We improve the model in three ways: First, we replace the rather simple Fourier series of the basic model by more general and flexible periodic Basis splines (B-splines). Second, we model conditional heteroscedasticity by a threshold-GARCH (TGARCH) model, one aspect that is entirely left out by the underlying model. Third, we evaluate several distributional forms of the model’s error term. While the original WPPT assumes gaussian errors only, we also investigate whether the errors may follow a Student’s t-distribution as well as a skew t-distribution. In this article we show that our periodic WPPT-CH model is able to improve forecasts’ accuracy significantly, when compared to the plain WPPT model.

An Implementation of the Mycielski Algorithm as a Predictor in R

Univariate time series analysis is usually performed by arbitrarily complex parametric modeling. At least for prediction, a simple non-parametric alternative is the Mycielski algorithm, a forecasting method based on pat- tern matching. The reproducible research presented here shows how to perform out of sample forecasts using the methodology of Mycielski. The algorithm provides well results in scenarios where usual univariate models such as ARIMA family models return limited accuracy. In this article we describe the idea of the Mycielski based prediction algorithm in general. We contribute a reference implementation in R and give a short example.

Demand for investment advice over time: the disposition effect revisited

Czarnitzki and Stadtmann (2005) measure the interdependence of demand for investment advice (approximated by sales of investor magazines) and stock prices. They find strong evidence that confirms the presence of the disposition effect, i.e. the empirical observation that investors sell winners (too) early and abide losers (too) long. We reinvestigate their findings and confirm that the effect is very well present in the formerly analysed time frame, but clearly wears off afterward. As an explanation for the decline, we provide three lines of argumentation and show that disposition effect might depend on the shareholder structure, which is in line with the theory.