330,463 research outputs found
Group integrative dynamic factor models for inter- and intra-subject brain networks
This work introduces a novel framework for dynamic factor model-based data
integration of multiple subjects, called GRoup Integrative DYnamic factor
models (GRIDY). The framework facilitates the determination of inter-subject
differences between two pre-labeled groups by considering a combination of
group spatial information and individual temporal dependence. Furthermore, it
enables the identification of intra-subject differences over time by employing
different model configurations for each subject. Methodologically, the
framework combines a novel principal angle-based rank selection algorithm and a
non-iterative integrative analysis framework. Inspired by simultaneous
component analysis, this approach also reconstructs identifiable latent factor
series with flexible covariance structures. The performance of the framework is
evaluated through simulations conducted under various scenarios and the
analysis of resting-state functional MRI data collected from multiple subjects
in both the Autism Spectrum Disorder group and the control group
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Time Series Forecasting with Many Predictors
We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the impact of irrelevant predictors. Moreover, a novel technique, called peeling, is used to boost the variable selection procedure so that many factor-relevant predictors can be included in prediction. Finally, the supervised dynamic principal component analysis (sdPCA) method is adopted to account for the dynamic information in factor recovery. In simulation studies, we found that the proposed method adapts well to unknown degrees of sparsity and factor strength, which results in good performance, even when the number of relevant predictors is large compared to the sample size. Applying to economic and environmental studies, the proposed method consistently performs well compared to some commonly used benchmarks in one-step-ahead out-sample forecasts
Functional dynamic factor models with application to yield curve forecasting
Accurate forecasting of zero coupon bond yields for a continuum of maturities
is paramount to bond portfolio management and derivative security pricing. Yet
a universal model for yield curve forecasting has been elusive, and prior
attempts often resulted in a trade-off between goodness of fit and consistency
with economic theory. To address this, herein we propose a novel formulation
which connects the dynamic factor model (DFM) framework with concepts from
functional data analysis: a DFM with functional factor loading curves. This
results in a model capable of forecasting functional time series. Further, in
the yield curve context we show that the model retains economic interpretation.
Model estimation is achieved through an expectation-maximization algorithm,
where the time series parameters and factor loading curves are simultaneously
estimated in a single step. Efficient computing is implemented and a
data-driven smoothing parameter is nicely incorporated. We show that our model
performs very well on forecasting actual yield data compared with existing
approaches, especially in regard to profit-based assessment for an innovative
trading exercise. We further illustrate the viability of our model to
applications outside of yield forecasting.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS551 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A New Estimative Current Mode Control Technique for DC-DC Converters Operating in Discontinuous Conduction Mode
A new digital control technique for power converters operating in discontinuous conduction mode (DCM) is introduced and applied to a boost converter. In contrast to the conventional analogue control methods, the principal idea of this new control scheme is to use real-time analysis and estimate the required on-time of the switch based on the dynamic of the system. The proposed control algorithm can easily be programmed in a digital signal processor (DSP). This novel technique is applicable to any converter operating in DCM including power factor correctors (PFC). However, this work mainly focuses on the boost topology. In this paper, the main mathematical concept of the new control algorithm is introduced, as well as the robustness investigation of the proposed method, simulation, and experimental results
Dynamic Inference in Probabilistic Graphical Models
Probabilistic graphical models, such as Markov random fields (MRFs), are
useful for describing high-dimensional distributions in terms of local
dependence structures. The probabilistic inference is a fundamental problem
related to graphical models, and sampling is a main approach for the problem.
In this paper, we study probabilistic inference problems when the graphical
model itself is changing dynamically with time. Such dynamic inference problems
arise naturally in today's application, e.g.~multivariate time-series data
analysis and practical learning procedures.
We give a dynamic algorithm for sampling-based probabilistic inferences in
MRFs, where each dynamic update can change the underlying graph and all
parameters of the MRF simultaneously, as long as the total amount of changes is
bounded. More precisely, suppose that the MRF has variables and
polylogarithmic-bounded maximum degree, and independent samples are
sufficient for the inference for a polynomial function . Our
algorithm dynamically maintains an answer to the inference problem using
space cost, and incremental
time cost upon each update to the MRF, as long as the well-known
Dobrushin-Shlosman condition is satisfied by the MRFs. Compared to the static
case, which requires time cost for redrawing all
samples whenever the MRF changes, our dynamic algorithm gives a
-factor speedup. Our approach relies on a
novel dynamic sampling technique, which transforms local Markov chains (a.k.a.
single-site dynamics) to dynamic sampling algorithms, and an "algorithmic
Lipschitz" condition that we establish for sampling from graphical models,
namely, when the MRF changes by a small difference, samples can be modified to
reflect the new distribution, with cost proportional to the difference on MRF
MC-ADAPT: Adaptive Task Dropping in Mixed-Criticality Scheduling
Recent embedded systems are becoming integrated systems with components of different criticality. To tackle this, mixed-criticality systems aim to provide different levels of timing assurance to components of different criticality levels while achieving efficient resource utilization. Many approaches have been proposed to execute more lower-criticality tasks without affecting the timeliness of higher-criticality tasks. Those previous approaches however have at least one of the two limitations; i) they penalize all lower-criticality tasks at once upon a certain situation, or ii) they make the decision how to penalize lowercriticality tasks at design time. As a consequence, they underutilize resources by imposing an excessive penalty on lowcriticality tasks. Unlike those existing studies, we present a novel framework, called MC-ADAPT, that aims to minimally penalize lower-criticality tasks by fully reflecting the dynamically changing system behavior into adaptive decision making. Towards this, we propose a new scheduling algorithm and develop its runtime schedulability analysis capable of capturing the dynamic system state. Our proposed algorithm adaptively determines which task to drop based on the runtime analysis. To determine the quality of task dropping solution, we propose the speedup factor for task dropping while the conventional use of the speedup factor only evaluates MC scheduling algorithms in terms of the worst-case schedulability. We apply the speedup factor for a newly-defined task dropping problem that evaluates task dropping solution under different runtime scheduling scenarios. We derive that MC-ADAPT has a speedup factor of 1.619 for task drop. This implies that MC-ADAPT can behave the same as the optimal scheduling algorithm with optimal task dropping strategy does under any runtime scenario if the system is sped up by a factor of 1.619
Towards Fair Disentangled Online Learning for Changing Environments
In the problem of online learning for changing environments, data are
sequentially received one after another over time, and their distribution
assumptions may vary frequently. Although existing methods demonstrate the
effectiveness of their learning algorithms by providing a tight bound on either
dynamic regret or adaptive regret, most of them completely ignore learning with
model fairness, defined as the statistical parity across different
sub-population (e.g., race and gender). Another drawback is that when adapting
to a new environment, an online learner needs to update model parameters with a
global change, which is costly and inefficient. Inspired by the sparse
mechanism shift hypothesis, we claim that changing environments in online
learning can be attributed to partial changes in learned parameters that are
specific to environments and the rest remain invariant to changing
environments. To this end, in this paper, we propose a novel algorithm under
the assumption that data collected at each time can be disentangled with two
representations, an environment-invariant semantic factor and an
environment-specific variation factor. The semantic factor is further used for
fair prediction under a group fairness constraint. To evaluate the sequence of
model parameters generated by the learner, a novel regret is proposed in which
it takes a mixed form of dynamic and static regret metrics followed by a
fairness-aware long-term constraint. The detailed analysis provides theoretical
guarantees for loss regret and violation of cumulative fairness constraints.
Empirical evaluations on real-world datasets demonstrate our proposed method
sequentially outperforms baseline methods in model accuracy and fairness.Comment: Accepted by KDD 202
A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation
Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme
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