Diagnosis of Brain Diseases via Multi-Scale Time-Series Model
نویسندگان
چکیده
منابع مشابه
Uncertainty Time Series' Multi-Scale Fractional-Order Association Model
This article first systematically classified the uncertainty and provided the multi-scale fractional ordered association model in accordance with the multiple uncertainty time series. From the mathematical point of view, the model used in this thesis extended the integerorder correlation measurement to the fractional-order correlation measurement; elongate the information recognition from point...
متن کاملIntegration of multi-time-scale models in time series forecasting
A solution to the problem of producing long-range forecasts on a short sampling interval is proposed. It involves the incorporation of information from a long sampling interval series, which could come from an independent source, into forecasts produced by a state-space model based on a short sampling interval. The solution is motivated by the desire to incorporate yearly electricity consumptio...
متن کاملA Class of Multi-Scale Time Series Models
We introduce a class of multi-scale models for time series. The novel framework couples ’simple’ standard Markov models for the time series stochastic process at different levels of aggregation, and links them via ’error’ models to induce a new and rich class of structured linear models reconciling modeling and information at different levels of resolution. Jeffrey’s rule of conditioning is use...
متن کاملAnalysis of Brain States from Multi-Region LFP Time-Series
The local field potential (LFP) is a source of information about the broad patterns of brain activity, and the frequencies present in these time-series measurements are often highly correlated between regions. It is believed that these regions may jointly constitute a “brain state,” relating to cognition and behavior. An infinite hidden Markov model (iHMM) is proposed to model the evolution of ...
متن کاملMulti-Scale Change Point Detection in Multivariate Time Series
A core problem in time series data is learning when things change. This problem is especially challenging when changes appear gradually and at varying timescales, such as in health. Convolutional Neural Networks (CNNs) have the potential to recognize and localize complex patterns, but are sensitive to scale. We propose a new class of scale and shift invariant neural networks that augment CNNs w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2019
ISSN: 1662-453X
DOI: 10.3389/fnins.2019.00197