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Model-free quantification of time-series predictability
2014
Physical Review E
This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-world time-series data---which results from the dimension, nonlinearity, and non-stationarity of the generating process, as well as from measurement issues like noise, aggregation, and finite data length---is both empirically quantifiable and directly correlated with predictability. In particular, we argue that
doi:10.1103/physreve.90.052910
pmid:25493861
fatcat:zubaye2c7zcqdkc7b23nlnlkoy