Detrended Cross-Correlation Coefficient with sliding windows. Description This function generates Detrended Cross-Correlation Coefficient of two time series with sliding windows approach. Usage rhodcca.SlidingWindows(x, y, w = 98, k = 10, nu = 0) Arguments x A vector containing univariate time series. y A vector containing univariate time series.

2019年8月1日 · In this paper, we propose to apply ρ D C C A with sliding windows approach to measure the cross-correlation between two time series dynamically.

2021年7月15日 · In this paper we implemented the detrended multiple cross-correlation coefficient with sliding windows approach in order to measure multiple cross-correlation between time series as a function of time scale n, and a sliding time τ.

Symmetric cross-correlation forces a tradeoff between small windows, allowing for increased sensitivity to nonstationarities, and large lag ranges, allowing a full appreciation of any periodicity or delay in the correlation.

2019年4月1日 · Analogous to a moving average function, a sliding window analysis computes a succession of pairwise correlation matrices using the time series from a given parcellation of brain regions.

2024年6月18日 · Using in vivo resting-state fMRI data, we first demonstrate the non-stationarity in both the cross-correlation function (XCF) and the autocorrelation function (ACF). Then, we propose the variance estimation of the sliding window …

2023年8月24日 · In this article, we present a novel 2-D timestamp-based sliding window cross correlation algorithm for a distributed optical fiber sensing interferometer based on shifting difference signal reconstruction.

2021年4月1日 · In this paper we implemented the detrended multiple cross-correlation coefficient with sliding windows approach in order to measure multiple cross-correlation between time series as a function...

2019年4月1日 · We propose the Multifractal detrended cross-correlation heatmaps (MF-DCCHM) based on the DCCA cross-correlation coefficients with sliding boxes, a systematic approach capable of mapping...

2023年11月22日 · Our tool consists of two parts: the sliding-window cross-correlation (SWCC) method and sliding-window mutual information (SWMI) method. As their names suggest, both parts involve a set of sliding windows.