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Found 14 papers, 2 papers with code
Efficient Interpretable Nonlinear Modeling for Multiple Time Series
Predictive linear and nonlinear models based on kernel machines or deep neural networks have been used to discover dependencies among time series.
Consistent Signal Reconstruction from Streaming Multivariate Time Series
In this paper, we formalize for the first time the concept of consistent signal reconstruction from streaming time-series data.
An Online Multiple Kernel Parallelizable Learning Scheme
The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel.
A Trainable Approach to Zero-delay Smoothing Spline Interpolation
As a result, a zero-delay interpolation is achieved in exchange for an almost certainly higher cumulative cost as compared to interpolating all data samples together.
Random Feature Approximation for Online Nonlinear Graph Topology Identification
Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear.
Explainable nonlinear modelling of multiple time series with invertible neural networks
A method for nonlinear topology identification is proposed, based on the assumption that a collection of time series are generated in two steps: i) a vector autoregressive process in a latent space, and ii) a nonlinear, component-wise, monotonically increasing observation mapping.
Online Non-linear Topology Identification from Graph-connected Time Series
Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and finance engineering.
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data
The inexact proximal online gradient descent framework is used to derive a performance guarantee for the proposed algorithm, in the form of a dynamic regret bound.
Channel Gain Cartography via Mixture of Experts
In this work, apart from adapting the location-free features for the CG maps, a method that can combine both approaches is proposed in a mixture-of-experts framework.
Fast Decentralized Linear Functions Over Edge Fluctuating Graphs
In contrast, this paper develops a framework for computing a wide class of linear transformations in a decentralized fashion by relying on the notion of graph shift operator.
Accurate Graph Filtering in Wireless Sensor Networks
Wireless sensor networks (WSNs) are considered as a major technology enabling the Internet of Things (IoT) paradigm.
Online Hyperparameter Search Interleaved with Proximal Parameter Updates
Previously existing algorithms that efficiently search for hyperparameters relying on the smoothness of the cost function cannot be applied in problems such as Lasso regression.
Online Topology Identification from Vector Autoregressive Time Series
Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection.
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements.
