Understanding and predicting the dynamics of complex systems are central goals for many scientific disciplines (Weigend and Gershenfeld 1993, Hofman et al. Intrinsic predictability also provides a model-free baseline of forecasting proficiency against which modeling efforts can be evaluated.
#INTRINSIC DARK NOISE SERIES#
Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. These results demonstrate a theoretically grounded basis for a model-free evaluation of a system's intrinsic predictability. We show how deviations from the expected PE–FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics. This relationship is verified for a data set of 461 empirical ecological time series. By means of simulations, we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. Intrinsic predictability may be quantified with permutation entropy (PE), a model-free, information-theoretic measure of the complexity of a time series. Ideally, model proficiency would be judged with respect to the systems’ intrinsic predictability, the highest achievable predictability given the degree to which system dynamics are the result of deterministic vs. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Model forecasting error (FE) is the usual measure of success however model predictions provide no insights into the potential for improvement. Successfully predicting the future states of systems that are complex, stochastic, and potentially chaotic is a major challenge.
0 kommentar(er)
