Convergent cross mapping is a principled causal discovery technique for signals, but its
efficacy depends on a number of assumptions about the systems that generated the signals …

Abstract In 2012, the Convergent Cross Mapping method for finding a causal relationship
between system variables from their time series was published. This method is widely used …

Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be
continuously monitored using Cardiotocography (CTG). This is the most widely adopted …

In this paper, we extend convergent cross mapping (CCM) and propose symbolic CCM
(SCCM), which uses mutual information based on permutation pattern instead of Pearson …

Detection of anomalies in time series is still a challenging problem. In this paper, we provide
a new approach to unsupervised detection of anomalies in time series based on the concept …

Causal discovery with time series data remains a challenging yet increasingly important task
across many scientific domains. Convergent cross mapping (CCM) and related methods …

Understanding causal relationships within a system is crucial for uncovering its underlying
mechanisms. Causal discovery methods, which facilitate the construction of such models …

Estimating and surveillance volumes of patients are of great importance for public health
and resource allocation. In many situations, the change of these volumes is correlated with …

Falsification is the basis for testing existing hypotheses, and a great danger is posed when
results incorrectly reject our prior notions (false positives). Though nonparametric and …

Abstract Small and Medium Enterprises (SMEs) represent 90% of businesses and more than
50% of employment worldwide. While large companies lead long-term innovation strategies …