A prediction scheme using perceptually important points and dynamic time warping

• A novel, prediction scheme combining two celebrated data mining tools is proposed.
• PIPs are used to dynamically segment price series into subsequences.
• Dynamic time warping (DTW) is used to find similar historical subsequences.
• Predictions are made based from the mappings of the most similar subsequences.
• The proposed algorithm captures the deterministic structure in examined series.

An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price.