1,211 research outputs found
Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange
Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version
Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series
Theoretic analysis shows that the output power of the distributed generation system is nonlinear and chaotic. And it is coupled with the microenvironment meteorological data. Chaos is an inherent property of nonlinear dynamic system. A predicator of the output power of the distributed generation system is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space. Firstly, chaos should be detected and quantified for the intensive studies of nonlinear systems. If the largest Lyapunov exponent is positive, the dynamical system must be chaotic. Then, the embedding dimension and the delay time are chosen based on the improved C-C method. The attractor of chaotic power time series can be reconstructed based on the embedding dimension and delay time in the phase space. By now, the neural network can be trained based on the training samples, which are observed from the distributed generation system. The neural network model will approximate the curve of output power adequately. Experimental results show that the maximum power point of the distributed generation system will be predicted based on the meteorological data. The system can be controlled effectively based on the prediction
Methods to improve neural network performance in daily flows prediction
Author name used in this publication: K. W. Chau2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Tracking the weight of Hurricane Harvey’s stormwater using GPS data
On 26 August 2017, Hurricane Harvey struck the Gulf Coast as a category four cyclone depositing ~95 km3 of water, making it the wettest cyclone in U.S. history. Water left in Harvey’s wake should cause elastic loading and subsidence of Earth’s crust, and uplift as it drains into the ocean and evaporates. To track daily changes of transient water storage, we use Global Positioning System (GPS) measurements, finding a clear migration of subsidence (up to 21 mm) and horizontal motion (up to 4 mm) across the Gulf Coast, followed by gradual uplift over a 5-week period. Inversion of these data shows that a third of Harvey’s total stormwater was captured on land (25.7 ± 3.0 km3 ), indicating that the rest drained rapidly into the ocean at a rate of 8.2 km3 /day, with the remaining stored water gradually lost over the following 5 weeks at ~1 km3 /day, primarily by evapotranspiration. These results indicate that GPS networks can remotely track the spatial extent and daily evolution of terrestrial water storage following transient, extreme precipitation events, with implications for improving operational flood forecasts and understanding the response of drainage systems to large influxes of water
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation
Probabilistic time series forecasting predicts the conditional probability
distributions of the time series at a future time given past realizations. Such
techniques are critical in risk-based decision-making and planning under
uncertainties. Existing approaches are primarily based on parametric or
semi-parametric time-series models that are restrictive, difficult to validate,
and challenging to adapt to varying conditions. This paper proposes a
nonparametric method based on the classic notion of {\em innovations} pioneered
by Norbert Wiener and Gopinath Kallianpur that causally transforms a
nonparametric random process to an independent and identical uniformly
distributed {\em innovations process}. We present a machine-learning
architecture and a learning algorithm that circumvent two limitations of the
original Wiener-Kallianpur innovations representation: (i) the need for known
probability distributions of the time series and (ii) the existence of a causal
decoder that reproduces the original time series from the innovations
representation. We develop a deep-learning approach and a Monte Carlo sampling
technique to obtain a generative model for the predicted conditional
probability distribution of the time series based on a weak notion of
Wiener-Kallianpur innovations representation. The efficacy of the proposed
probabilistic forecasting technique is demonstrated on a variety of electricity
price datasets, showing marked improvement over leading benchmarks of
probabilistic forecasting techniques
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