Data Mining for Temporal Data Analysis

Dear Colleagues,

Temporal data in general and times series in particular are ubiquitous in our current world. They are recorded from various sensors in many application domains ranging from bio-informatics, computer vision, natural language processing, … to medicine, finance or engineering (as a mean to build, for example, smart cities). Contrarily to static data, temporal data are of complex nature, they are generally noisy, of high dimensionality, they may be non-stationary, they may have several invariant domain-dependent factors as time delay, translation, scale or trend effects. These temporal peculiarities pose a challenge to standard statistical models and machine learning approaches, that mainly assume i.i.d data, homoscedasticity, normality of residuals, etc.

To tackle such challenging data, we invite our colleagues to submit papers that propose new advanced approaches at the intersection of statistics, time series analysis, signal processing and machine learning.

Prof. Dr. Élisa Fromont
Guest Editor

Manuscript Submission Information

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