A Challenge to Prediction Experts

Here is another prediction quiz, again suggested by Demetris Koutsoyiannis, a little different to the one that challenged readers here. In this case the quiz is not to guess the underlying model (exact solution) but to find an assumed model or technique of any type that can give good predictions based on the past statistical behaviour, autocorrelation, or perhaps reconstructed dynamics (in case of ANN or chaotic nonlinear methods).

The time series given in the text file neomail.txt (500 values) was generated by a
mathematical model. The time series is characterized by strong autocorrelation and perhaps long term persistence. At this time the model type is not disclosed but it uses a single algorithm whose application can be continued to give at least 50 more data values (the “true” values). These will be disclosed along with the model at the end of the quiz. Meanwhile, can you predict them?


Here is the sequence of values generated
by a deterministic algorithm. The challenge is to accurately predict the
next 50 values.

The criterion to select the best answer will be the least mean square error between true and predicted values. Two prizes are foreseen: First prize for the best long-term prediction, for steps 1 to 50. Second prize for the best short-term prediction for steps 1 to 5.

Note 1: The part of the series between time steps (about) 100-250 may correlate relatively well with the CRU temperature series of the northern hemisphere – but this is a coincidence.

Note 2: All types of prediction models, simple logic, statistical,
stochastic, deterministic, chaotic, neural networks etc. are allowed.


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