Antipredictable Sequences: Harder to Predict Than Random Sequences

Huaiyu Zhu, Wolfgang Kinzel
1998 Neural Computation  
For any discrete-state sequence prediction algorithm A, it is always possible, using an algorithm B no more complicated than A, to generate a sequence for which A's prediction is always wrong. For any prediction algorithm A and sequence x, there exists a sequence y no more complicated than x, such that if A performs better than random on x, then it will perform worse than random on y by the same margin. An example of a simple neural network predicting a bit sequence is used to illustrate this
more » ... ry general but not widely recognized phenomenon. This implies that any predictor with good performance must rely on some (usually implicitly) assumed prior distributions of the problem.
doi:10.1162/089976698300017043 pmid:9804679 fatcat:vk3og6nnxzhnnal5lkuafurwqu