scholarly journals Forecasting High-Dimensional Financial Functional Time Series: An Application to Constituent Stocks in Dow Jones Index

2021 ◽  
Vol 14 (8) ◽  
pp. 343
Author(s):  
Chen Tang ◽  
Yanlin Shi
Keyword(s):  
Time Series ◽  
Factor Model ◽  
High Dimensional ◽  
Matrix Structure ◽  
New Approach ◽  
Jones Index ◽  
Share Prices ◽  
Functional Time Series ◽  
The Matrix ◽  
Highly Correlated

Financial data (e.g., intraday share prices) are recorded almost continuously and thus take the form of a series of curves over the trading days. Those sequentially collected curves can be viewed as functional time series. When we have a large number of highly correlated shares, their intraday prices can be viewed as high-dimensional functional time series (HDFTS). In this paper, we propose a new approach to forecasting multiple financial functional time series that are highly correlated. The difficulty of forecasting high-dimensional functional time series lies in the “curse of dimensionality.” What complicates this problem is modeling the autocorrelation in the price curves and the comovement of multiple share prices simultaneously. To address these issues, we apply a matrix factor model to reduce the dimension. The matrix structure is maintained, as information contains in rows and columns of a matrix are interrelated. An application to the constituent stocks in the Dow Jones index shows that our approach can improve both dimension reduction and forecasting results when compared with various existing methods.

scholarly journals Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics

2021 ◽  
Vol 15 ◽  
Author(s):  
Ashkan Faghiri ◽  
Eswar Damaraju ◽  
Aysenil Belger ◽  
Judith M. Ford ◽  
Daniel Mathalon ◽  
...  
Keyword(s):  
Time Series ◽  
Dimensional Space ◽  
Real Data ◽  
High Dimensional ◽  
Data Set ◽  
High Dimensional Space ◽  
New Approach ◽  
Oscillatory Pattern ◽  
Functional Dynamics ◽  
Novel Approach

BackgroundA number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.MethodsWe introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.ResultsWe present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.ConclusionOur proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.

Formation of Persistent Slip Band in Fatigued Copper Single Crystals

1982 ◽  
Vol 40 ◽  
pp. 470-471
Author(s):  
N. Y. Jin
Keyword(s):  
Volume Fraction ◽  
Fatigue Cracks ◽  
Wall Structure ◽  
Matrix Structure ◽  
Dislocation Dipole ◽  
Slip Bands ◽  
Persistent Slip Bands ◽  
The Matrix ◽  
Hard Shell ◽  
Copper Single Crystals

Localised plastic deformation in Persistent Slip Bands(PSBs) is a characteristic feature of fatigue in many materials. The dislocation structure in the PSBs contains regularly spaced dislocation dipole walls occupying a volume fraction of around 10%. The remainder of the specimen, the inactive "matrix", contains dislocation veins at a volume fraction of 50% or more. Walls and veins are both separated by regions in which the dislocation density is lower by some orders of magnitude. Since the PSBs offer favorable sites for the initiation of fatigue cracks, the formation of the PSB wall structure is of great interest. Winter has proposed that PSBs form as the result of a transformation of the matrix structure to a regular wall structure, and that the instability occurs among the broad dipoles near the center of a vein rather than in the hard shell surounding the vein as argued by Kulmann-Wilsdorf.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

scholarly journals On the Modular Operator of Mutli-component Regions in Chiral CFT

2021 ◽  
Author(s):  
Stefan Hollands
Keyword(s):  
Field Theory ◽  
Integral Equation ◽  
Hilbert Problem ◽  
Matrix Elements ◽  
New Approach ◽  
Conformal Field ◽  
The Matrix ◽  
Kms Condition ◽  
Modular Operator ◽  
Riemann Hilbert Problem

AbstractWe introduce a new approach to find the Tomita–Takesaki modular flow for multi-component regions in general chiral conformal field theory. Our method is based on locality and analyticity of primary fields as well as the so-called Kubo–Martin–Schwinger (KMS) condition. These features can be used to transform the problem to a Riemann–Hilbert problem on a covering of the complex plane cut along the regions, which is equivalent to an integral equation for the matrix elements of the modular Hamiltonian. Examples are considered.

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