Data-driven models for monthly streamflow time series prediction

TL;DR: Wang et al. as mentioned in this paper proposed an ensemble empirical mode decomposition (EEMD)-ARIMA model for forecasting annual runoff time series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir in China.

TL;DR: In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.

TL;DR: This proposed approach, believed to have taken full advantage of the strengths of both filter and wrapper methods, first uses the Partial Mutual Information based filter method to filter out most of the irrelevant and redundant features, and subsequently applies a wrapper method to further reduce the redundant features without degrading the forecasting accuracy.

TL;DR: Empirical results demonstrate that the proposed novel ensemble learning paradigm statistically outperforms all considered benchmark models in both prediction accuracy and effectiveness and is a promising tool to predict complicated time series with high volatility and irregularity.

TL;DR: In this paper, the correlation exponent v is introduced as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise, and algorithms for extracting v from the time series of a single variable are proposed.

TL;DR: In this paper, the mutual information I is examined for a model dynamical system and for chaotic data from an experiment on the Belousov-Zhabotinskii reaction.

TL;DR: This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.

TL;DR: In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.