Trend change prediction in complex systems with a large number of noisy time
series is a problem with many applications for real-world phenomena, with stock
markets as a notoriously difficult to predict example of such systems. We
approach predictions of directional trend changes via complex lagged
correlations between them, excluding any information about the target series
from the respective inputs to achieve predictions purely based on such
correlations with other series. We propose the use of deep neural networks that
employ step-wise linear regressions with exponential smoothing in the
preparatory feature engineering for this task, with regression slopes as trend
strength indicators for a given time interval. We apply this method to
historical stock market data from 2011 to 2016 as a use case example of lagged
correlations between large numbers of time series that are heavily influenced
by externally arising new information as a random factor. The results
demonstrate the viability of the proposed approach, with state-of-the-art
accuracies and accounting for the statistical significance of the results for
additional validation, as well as important implications for modern financial
economics.Comment: 11 pages, 4 figure
