相关论文
2019 - International Journal of Forecasting
A Functional Time Series Analysis of Forward Curves Derived from Commodity Futures

We study forward curves formed from commodity futures prices listed on the Standard and Poor's-Goldman Sachs Commodities Index (S&P GSCI) using recently developed tools in functional time series analysis. Functional tests for stationarity and serial correlation suggest that log-differenced forward curves may be generally considered as stationary and conditionally heteroscedastic sequences of functions. Several functional methods for forecasting forward curves that more accurately reflect the time to expiry of contracts are developed, and we found that these typically outperformed their multivariate counterparts, with the best among them using the method of predictive factors introduced by Kargin and Onatski (2008).

2012 - Journal of Software
JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis

To solve problems in the existing JPEG steganalysis schemes,such as high redundancy in features and failure to make good use of the complementarity among them,this study proposes a JPEG steganalysis approach based on feature fusion by the principal component analysis(PCA) and analysis of the complementarity among features.The study fuses complementary features to reflect the statistical differences between cover and stego signals in the round,isolates redundant components by PCA,and finally achieves the goal of improving accuracy.Experimental results show that in various datasets and embedding rates,this scheme provides more accuracy than the main JPEG steganalysis schemes against steganographic methods of high concealment(e.g.F5,MME and PQ) and greatly reduces the time cost of the existing fusion methods on feature level.

2010 - ECCV Workshops
Hand Modeling and Tracking for Video-Based Sign Language Recognition by Robust Principal Component Analysis

Hand modeling and tracking are essential in video-based sign language recognition. The high reformability and the large number of degrees of freedom of hands render the problem difficult. To tackle these challenges, a novel approach based on robust principal component analysis (PCA) is proposed. The robust PCA incorporates an L1 norm objective function to deal with background clutter, and a projection pursuit strategy to deal with the lack of alignment due to the deformation of hands. The learning algorithm of the robust PCA is very simple, involving only a search for the solutions in a finite set constructed from the training data, which leads to the learning of much more representative and interpretable bases. The incorporation of the L1 regularization in the fitting of the learned robust PCA models results in cleaner reconstructions and more stable fitting. Based on the robust PCA, a hand tracking system is developed that contains a skin-color region segmentation based on graph cuts and template matching in the framework of particle filtering. Experiments on a publicly available sign-language video database demonstrates the strength of the method.

2012 - International Journal of Computer Applications
Urdu Character Recognition using Principal Component Analysis

This paper proposes a method for Urdu language text search in image based Urdu Text. In the proposed method two databases of images have been created; first one for training purpose and another for testing purpose. Training database is named ‘TrainDatabase’ and testing database as ‘TestDatabase’. Training database consists of all characters of Urdu language in different shapes. Eigen values and Eigen vectors of all the images to be placed in the TrainingDatabase are calculated. Only those values having highest Eigen values are kept. A feature vector for each image of the TrainDatabase is calculated by the algorithm. A threshold value is chosen such that it defines maximum allowable distance between TrainDatabase and TestDatabase images. Feature vector is also created for each image to be identified and placed in ‘TestDatabase’. Comparison is done for a character to be identified with each image of ‘TrainDatabase’. If the character to be recognized is matching with any character of the TrainDatabase result is shown by algorithm. MATLAB has been used as a simulation tool and the recognition rate obtained was 96.2 % for isolated characters. General Terms Pattern Recognition, Optical Character Recognition.

2012 - 2012 IEEE Systems and Information Engineering Design Symposium
Targeted advertising in the online video space

Videology is an online video advertising, optimization, and yield management solutions provider whose business strategy is to purchase online video advertising inventory from content providers and deliver ads to their clients using behavioral and demographic targeting to maximize value. The Capstone team used a diverse set of analytical tools, including Time Series models, Principal Component analysis, Decision Tree Optimization, Generalized Linear Models, K-Means clustering, and linear programming to improve efficiency in the online video advertisement space by connecting consumers with advertisements on which they are most likely to click. Trends, patterns, and anomalies in the data may lead to new targeting opportunities for Videology.

2015 - International Journal of Computer Applications
Brain Tumor Classification using Principal Component Analysis and Probabilistic Neural Network

growth of the cell in the brain is the brain tumor. Brain tumor is common and serious disease. The proposed method for tumor classification in magnetic resonance brain image is the human inspection. Magnetic Resonance Imaging (MRI) plays an intrinsic role in the brain tumor disease diagnostic application. Various types of tumor that leads decision complicated. So that correct classification of brain tumor is important to detect the types of tumor. In this paper, Probabilistic Neural network (PNN) is used for brain tumor classification. Decision making was performed in two steps: 1) Feature extraction using Principal Component Analysis (PCA). And 2) Classification is done by Probabilistic neural network (PNN). Brain tumor is classified into three classes: Normal, Benign and Malignant. Again malignant tumor is classified as Glioma and Meningioma. PNN is faster and provide good classification accuracy.

2015 - Hydrological Processes
Temporal trends in δ18O composition of precipitation in Germany: insights from time series modelling and trend analysis

Temporal and spatial variations of stable oxygen (18O) and hydrogen (2H) isotope measurements in precipitation act as important proxies for changing hydro-meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of δ18O in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann–Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend-free pre-whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright © 2014 John Wiley & Sons, Ltd.

2015 - Geophysical Journal International
Calibrating interferometric synthetic aperture radar (InSAR) images with regional GPS network atmosphere models

SUMMARY Interferometric synthetic aperture radar (InSAR) technology provides a valuable tool for obtaining Earth surface deformation and topography at high spatial resolution for crustal deformation studies. Similar to global positioning system (GPS), InSAR measurements are affected by the Earth’s ionospheric and tropospheric layers as the electromagnetic signals significantly refract while propagating through the different layers. While GPS signals propagating through the neutral atmosphere are affected primarily by the distribution, pressure and temperature of atmospheric gases, including water vapour, the propagation through the ionosphere is mainly affectedbythenumberoffreeelectronsalongthesignalpath.Here,wepresenttheuseofdense regional GPS networks for extracting tropospheric zenith delays and ionospheric total electron content (TEC) maps in order to reduce the noise levels in InSAR images. The results show significant reduction in the root mean square (RMS) values when simultaneously combining the two corrections, both at short time periods where no surface deformation is expected, and at longer periods, where imaging of localized subsidence and fault creep is enhanced.

2016 - 2016 IEEE 55th Conference on Decision and Control (CDC)
Dynamic tensor time series modeling and analysis

In this paper we propose a model reduction and identification approach for multilinear dynamical system (MLDS) driven by noise. Compared to standard linear dynamical system based approaches which fit vector or matrix models to tensor time series, MLDS provides more natural, compact and accurate representation of tensorial data with fewer model parameters. The proposed algorithm for identifying MLDS parameters employs techniques from multilinear subspace learning: mulilinear Principal Component Analysis and multilinear regression. In addition compact array normal distribution is used to represent and estimate model error and output noise.We illustrate the benefits of the proposed approach on some real world datasets.

2014 - IEEE Transactions on Geoscience and Remote Sensing
SAR Image Denoising via Clustering-Based Principal Component Analysis

The combination of nonlocal grouping and transformed domain filtering has led to the state-of-the-art denoising techniques. In this paper, we extend this line of study to the denoising of synthetic aperture radar (SAR) images based on clustering the noisy image into disjoint local regions with similar spatial structure and denoising each region by the linear minimum mean-square error (LMMSE) filtering in principal component analysis (PCA) domain. Both clustering and denoising are performed on image patches. For clustering, to reduce dimensionality and resist the influence of noise, several leading principal components identified by the minimum description length criterion are used to feed the K-means clustering algorithm. For denoising, to avoid the limitations of the homomorphic approach, we build our denoising scheme on additive signal-dependent noise model and derive a PCA-based LMMSE denoising model for multiplicative noise. Denoised patches of all clusters are finally used to reconstruct the noise-free image. The experiments demonstrate that the proposed algorithm achieved better performance than the referenced state-of-the-art methods in terms of both noise reduction and image detail preservation.

2002 - Chinese Journal of Geophysics
Present-time crustal deformation in China and its surrounding regions by GPS

Applying recent 10-year GPS measurement data from some nationwide GPS networks, several regional GPS monitoring networks and the Asia-Pacific Regional Geodetic Project (APRGP), this paper gives out a combined, consistent and unified velocity field solution in ITRF97 by rotation transformation of regional GPS network results. In order to study the characteristics of present-time intra-plate crustal deformation of China, we have constructed a new present-time plate motion model named ITRF97VEL. The model can better describe present-time features of global plate motion than the geological model NNR-NUVEL1A. Based on the present time plate motion model ITRF97VEL, deformation velocities of more than 260 GPS sites are determined. The results show the crustal motion in China is especially inhomogeneous. The crustal deformation in the west of China is far stronger and more complicated than that in the east of China taking the N-S seismic belt of China as a boundary. The deformation velocity gradually reduces from south to north in the west of China by energy release in several W-E direction arc suture zones. The Qinghai-Xizang Plateau is shortening in north-south direction and extending in west-east direction due to the strong shove of Indian plate.The shortening of about 15mm/a and 9–13mm/a is accommodated across Hymalayan block and the west Tian Shan respectively. Within southern Tibet,between the longitudes of 80°E to 91°E,there is E-W extension of 20.2±1:2 mm/a. The slip rates of KJFZ in south Tibet and Altyn Tagh fault are 2–3mm/a and 4–6 mm/a respectively. Our GPS results indicate there is a less than 7mm/a shortening within the Longmen Shan. These results support the supposition of crustal thickening.

2012 - ArXiv
Uncertain Time-Series Similarity: Return to the Basics

In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others. Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms. In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach, and further compare these alternatives with two additional techniques that were carefully studied before. We conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results, which suggest that a fruitful research direction is to take into account the temporal correlations in the time series. Based on our evaluations, we also provide guidelines useful for the practitioners in the field.

2004
Classification of process dynamics with monte carlo singular spectrum analysis

Singular spectrum analysis is a linear multivariate method for the analysis of time series data, based on principal component analysis of an augmented data set consisting of the original time series data and lagged copies of the data. It can be used to decompose the time series into a set of component time series, each of which could be investigated individually to gain a better understanding of the process dynamics, or to allow for the removal of noise from the data.

2009 - Fuzzy Sets Syst.
Autocorrelation-based fuzzy clustering of time series

The traditional approaches to clustering a set of time series are generally applicable if there is a fixed underlying structure to the time series so that each will belong to one cluster or the other. However, time series often display dynamic behaviour in their evolution over time. This dynamic behaviour should be taken into account when attempting to cluster time series. For instance, during a certain period, a time series might belong to a certain cluster; afterwards its dynamics might be closer to that of another cluster. In this case, the traditional clustering approaches are unlikely to find and represent the underlying structure in the given time series. This switch from one time state to another, which is typically vague, can be naturally treated following a fuzzy approach. This paper proposes a fuzzy clustering approach based on the autocorrelation functions of time series, in which each time series is not assigned exclusively to only one cluster, but it is allowed to belong to different clusters with various membership degrees.

2022
A Dissimilarity Measure for Comparing Subsets of Data : Application to Multivariate Time Series ∗

Similarity is a central concept in data mining. Many techniques, such as clustering and classification, use similarity or distance measures to compare various subsets of multivariate data. However, most of these measures are only designed to find the distances between a pair of records or attributes in a data set, and not for comparing whole data sets against one another. In this paper we present a novel dissimilarity measure based on principal component analysis for doing such comparisons between such data sets, and in particular time series data sets. Our measure accounts for the correlation structure of the data, and can be tuned by the user to account for domain knowledge. Our measure is useful in such applications as change point detection, anomaly detection, and clustering in fields such as intrusion detection, clinical trial data analysis, and stock analysis.

2016 - Agricultural and Forest Meteorology
Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

Abstract The scale mismatch between remote sensing observations and state variables simulated by crop growth models decreases the reliability of crop yield estimates. To overcome this problem, we implemented a two-step data-assimilation approach: first, we generated a time series of 30-m-resolution leaf area index (LAI) by combining Moderate Resolution Imaging Spectroradiometer (MODIS) data and three Landsat TM images with a Kalman filter algorithm (the synthetic KF LAI series); second, the time series were assimilated into the WOFOST crop growth model to generate an ensemble Kalman filter LAI time series (the EnKF-assimilated LAI series). The synthetic EnKF LAI series then drove the WOFOST model to simulate winter wheat yields at 1-km resolution for pixels with wheat fractions of at least 50%. The county-level aggregated yield estimates were compared with official statistical yields. The synthetic KF LAI time series produced a more realistic characterization of LAI phenological dynamics. Assimilation of the synthetic KF LAI series produced more accurate estimates of regional winter wheat yield ( R 2  = 0.43; root-mean-square error (RMSE) = 439 kg ha −1 ) than three other approaches: WOFOST without assimilation (determination coefficient R 2  = 0.14; RMSE = 647 kg ha −1 ), assimilation of Landsat TM LAI ( R 2  = 0.37; RMSE = 472 kg ha −1 ), and assimilation of S-G filtered MODIS LAI ( R 2  = 0.49; RMSE = 1355 kg ha −1 ). Thus, assimilating the synthetic KF LAI series into the WOFOST model with the EnKF strategy provides a reliable and promising method for improving regional estimates of winter wheat yield.

2016 - Comput. Ind. Eng.
A wavelet-based clustering of multivariate time series using a Multiscale SPCA approach

Case study comprising the fault detection in a benchmark industrial process.Comprehensive and novel method for clustering multivariate time series.Multiscale approach provides improvement in clustering quality.Multiscale approach represents a useful technique in FDD problems.Results show the ability of the method in recognizing normal and fault patterns. Clustering and pattern recognition from data can be used as means to extract knowledge of a process which may be useful for control, predicting failures and supporting decision making, among other functions. This paper presents a method to recognize patterns in multivariate time series based on a combination of wavelet features, PCA (Principal Component Analysis) similarity metrics and fuzzy clustering. The signal analysis of some process variables is performed based on the Wavelet Transform (WT), and a Multiscale PCA Similarity factor (SPCAms) is proposed to consider the distances between objects (multivariate time series) according to a multi-resolution approach. A database extracted from the benchmark Tennessee Eastman (TE) process is used to show the efficiency of the method compared with traditional approaches in a fault detection and diagnosis problem. The clustering using SPCAms provides the recognition of a fault pattern which may be useful to support decision-making at the operational level allowing real-time monitoring of failure probability.

2020 - International Journal of Forecasting
The M4 Competition: 100,000 time series and 61 forecasting methods

Abstract The M4 Competition follows on from the three previous M competitions, the purpose of which was to learn from empirical evidence both how to improve the forecasting accuracy and how such learning could be used to advance the theory and practice of forecasting. The aim of M4 was to replicate and extend the three previous competitions by: (a) significantly increasing the number of series, (b) expanding the number of forecasting methods, and (c) including prediction intervals in the evaluation process as well as point forecasts. This paper covers all aspects of M4 in detail, including its organization and running, the presentation of its results, the top-performing methods overall and by categories, its major findings and their implications, and the computational requirements of the various methods. Finally, it summarizes its main conclusions and states the expectation that its series will become a testing ground for the evaluation of new methods and the improvement of the practice of forecasting, while also suggesting some ways forward for the field.

2013
High Frequency Data and Volatility in Foreign Exchange Rates

Exchange rates, like many other financial time series, display substantial heteroscedasticity. This poses obstacles in detecting trends and changes. Understanding volatility becomes extremely important in studying financial time series. Unfortunately, estimating volatility from low-frequency data, such as daily, weekly, or monthly observations, is very difficult. The recent availability of ultra-high-frequency observations, such as tick-by-tick data, to large financial institutions creates a new possibility for the analysis of volatile time series. This article uses tick-by-tick foreign-exchange rates to explore this new type of data. Unlike low-frequency data, high-frequency data have extremely high negative first-order autocorrelation in their return. In this article, I propose a model that can explain the negative autocorrelation and a volatility estimator for high-frequency data. The daily and hourly volatility estimates of exchange rate show some interesting patterns.

2006 - International Journal of Climatology
Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series

The Italian monthly temperature (mean, maximum and minimum) and precipitation secular data set was updated and completely revised. Station density and metadata availability were greatly improved and the series were subjected to a detailed quality control and homogenisation procedure. The data homogenisation is described in detail. The bias affecting original data is quantified by studying the temporal evolution of the mean adjustments applied to the series and examined in the light of the stations history. The results stress the importance of homogenisation in climate change studies. The final data set was clustered into climatically homogeneous regions by means of a Principal Component Analysis. Yearly and seasonal trend analyses were performed both on regional average series and on the mean Italian series. The results highlight a positive trend for mean temperature of about 1 K per century all over Italy; it is generally higher for minimum temperature than for the maximum temperature. The progressive application of trend analysis shows that, in the last 50 years, behaviour is the opposite; the maximum temperature trend being stronger than that of the minimum temperature. This has led to a negative trend in the daily temperature range that for the last 50 years has become positive. Precipitation shows a decreasing tendency, even if low and rarely significant, the negative trend being only 5% per century on a yearly basis. Copyright  2006 Royal Meteorological Society.

Spatiotemporal filtering using principal component analysis and Karhunen-Loeve expansion approaches for regional GPS network analysis

Yehuda Bock
|
Peng Fang
|
Danan Dong
|
Sharon Kedar
|
Linette Miriawati Prawirodirdjo
|
Frank H. Webb
|
Paul F. Jamason
|
Y. Bock
|
F. Webb
|
D. Dong
|
S. Kedar
|
P. Fang
|
P. Jamason
|
L. Prawirodirdjo

Abstract:[1] Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering. We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

参考文献

[1]  W. Farrell.  Deformation of the Earth by surface loads , 1972 .

[2]  Yehuda Bock,et al.  Southern California permanent GPS geodetic array: Error analysis of daily position estimates and site velocities , 1997 .

[3]  Y. Bock,et al.  Observation and modeling of thermoelastic strain in Southern California Integrated GPS Network daily position time series , 2006 .

[4]  T. van Dam,et al.  Displacements of the Earth's surface due to atmospheric loading: Effects on gravity and baseline measurements , 1987 .

[5]  Yehuda Bock,et al.  Satellite interferometric observations of displacements associated with seasonal groundwater in the Los Angeles basin , 2002 .

[6]  Jean-François Crétaux,et al.  Annual vertical crustal motions predicted from surface mass redistribution and observed by space geodesy , 2001 .

[7]  Michael B. Heflin,et al.  Interseismic strain accumulation and anthropogenic motion in metropolitan Los Angeles , 2005 .

[8]  J. B. Rundle,et al.  Using Eigenpattern Analysis to Constrain Seasonal Signals in Southern California , 2004 .

[9]  F. Pollitz,et al.  Mantle Flow Beneath a Continental Strike-Slip Fault: Postseismic Deformation After the 1999 Hector Mine Earthquake , 2001, Science.

[10]  Yehuda Bock,et al.  Error analysis of continuous GPS position time series , 2004 .

[11]  É. Calais,et al.  Continuous GPS measurements across the Western Alps, 1996–1998 , 1999 .

[12]  Timothy Ian Melbourne,et al.  Rapid postseismic transients in subduction zones from continuous GPS , 2002 .

[13]  Paul Segall,et al.  Network strain filter: A new tool for monitoring and detecting transient deformation signals in GPS arrays , 2004 .

[14]  Yehuda Bock,et al.  Instantaneous global plate motion model from 12 years of continuous GPS observations , 2004 .

[15]  Michael Bevis,et al.  Motion and rigidity of the Pacific Plate and implications for plate boundary deformation , 2002 .

[16]  Kelin Wang,et al.  A Silent Slip Event on the Deeper Cascadia Subduction Interface , 2001, Science.

[17]  L. Rivera,et al.  Coseismic Deformation from the 1999 Mw 7.1 Hector Mine, California, Earthquake as Inferred from InSAR and GPS Observations , 2002 .

[18]  Kenneth W. Hudnut,et al.  Detection of crustal deformation from the Landers earthquake sequence using continuous geodetic measurements , 1993, Nature.

[19]  H. Scherneck.  A parametrized solid earth tide model and ocean tide loading effects for global geodetic baseline measurements , 1991 .

[20]  Tim Melbourne,et al.  Periodic Slow Earthquakes from the Cascadia Subduction Zone , 2002, Science.

[21]  Timothy H. Dixon,et al.  REVEL: A model for Recent plate velocities from space geodesy , 2002 .

[22]  T. Dixon,et al.  Noise in GPS coordinate time series , 1999 .

[23]  Yehuda Bock,et al.  GPS measurements of current crustal movements along the Dead Sea Fault , 2004 .

[24]  Kenneth W. Hudnut,et al.  Crustal deformation measured in Southern California , 1997 .

[25]  Kosuke Heki,et al.  Crustal velocity field of southwest Japan: Subduction and arc‐arc collision , 2001 .

[26]  Jeffrey T. Freymueller,et al.  Global Plate Velocities from the Global Positioning System , 1997 .

[27]  Y. Bock,et al.  Anatomy of apparent seasonal variations from GPS‐derived site position time series , 2001 .

[28]  J. Johansson,et al.  Continuous GPS measurements of postglacial adjustment in Fennoscandia 1. Geodetic results , 2002 .

[29]  H. Hornik,et al.  Towards an Integrated Global Geodetic Observing System (IGGOS) , 2000 .

[30]  Robert W. King,et al.  Estimating regional deformation from a combination of space and terrestrial geodetic data , 1998 .

[31]  J. Parker,et al.  Analysis and modeling of Southern California deformation , 2000 .

[32]  Herb Dragert,et al.  GPS‐determination of along‐strike variation in Cascadia margin kinematics: Implications for relative plate motion, subduction zone coupling, and permanent deformation , 2001 .

[33]  R. Nikolaidis.  Observation of geodetic and seismic deformation with the Global Positioning System , 2002 .

[34]  Yehuda Bock,et al.  Southern California permanent GPS geodetic array: Spatial filtering of daily positions for estimating coseismic and postseismic displacements induced by the 1992 Landers earthquake , 1997 .

[35]  Isao Naito,et al.  An impact of estimating tropospheric delay gradients on precise positioning in the summer using the Japanese nationwide GPS array , 2003 .

[36]  Kenneth W. Hudnut,et al.  Co-seismic displacements of the 1994 Northridge, California, earthquake , 1996, Bulletin of the Seismological Society of America.

[37]  Timothy Ian Melbourne,et al.  Precursory transient slip during the 2001 Mw = 8.4 Peru earthquake sequence from continuous GPS , 2002 .

[38]  William C. Hammond,et al.  Contemporary tectonic deformation of the Basin and Range province, western United States: 10 years of observation with the Global Positioning System , 2004 .

[39]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[40]  J. C. Savage.  Principal component analysis of interseismic deformation in southern California , 1995 .

[41]  G. Blewitt,et al.  A New Global Mode of Earth Deformation: Seasonal Cycle Detected , 2001, Science.

[42]  Geoffrey Blewitt,et al.  Evidence for Deep Magma Injection Beneath Lake Tahoe, Nevada-California , 2004, Science.

[43]  M. Heflin,et al.  Atmospheric pressure loading effects on Global Positioning System coordinate determinations , 1994 .

[44]  Michael B. Heflin,et al.  The effect of the second order GPS ionospheric correction on receiver positions , 2003 .

[45]  C. Demets,et al.  Crustal velocity field of Mexico from continuous GPS measurements, 1993 to June 2001: Implications for the neotectonics of Mexico , 2003 .

[46]  Kristy F. Tiampo,et al.  Linear pattern dynamics in nonlinear threshold systems , 2000 .

[47]  Christian Rocken,et al.  Calibration of antenna-radome and monument-multipath effect of GEONET—Part 2: Evaluation of the phase map by GEONET data , 2001 .

[48]  Walter H. F. Smith,et al.  New version of the generic mapping tools , 1995 .

[49]  Mike P. Stewart,et al.  Aliased tidal signatures in continuous GPS height time series , 2003 .

[50]  Paul Bodin,et al.  Using 1-Hz GPS Data to Measure Deformations Caused by the Denali Fault Earthquake , 2003, Science.

[51]  Gerald W. Bawden,et al.  Tectonic contraction across Los Angeles after removal of groundwater pumping effects , 2001, Nature.

[52]  Zuheir Altamimi,et al.  ITRF2000: A new release of the International Terrestrial Reference Frame for earth science applications , 2002 .

[53]  Yuri Fialko,et al.  Evidence of fluid-filled upper crust from observations of postseismic deformation due to the 1992 Mw7.3 Landers earthquake , 2004 .

[54]  Fred F. Pollitz,et al.  Mobility of continental mantle: Evidence from postseismic geodetic observations following the 1992 Landers earthquake , 2000 .

[55]  J. C. Savage.  Principal Component Analysis of Geodetically Measured Deformation in Long Valley Caldera, Eastern California, 1983–1987 , 1988 .

[56]  J Johansson.  Continuous GPS measurement of postglacial adjustment in Fennoscandia, 1. , 2002 .

[57]  Kenneth W. Hudnut,et al.  Continuous GPS Observations of Postseismic Deformation Following the 16 October 1999 Hector Mine, California, Earthquake (Mw 7.1) , 2002 .

[58]  Yehuda Bock,et al.  Detection of arbitrarily large dynamic ground motions with a dense high‐rate GPS network , 2004 .

[59]  T. P. Yunck,et al.  Origin of the International Terrestrial Reference Frame , 2003 .

[60]  Timothy H. Dixon,et al.  Refined kinematics of the Eastern California shear zone from GPS observations, 1993-1998 , 2001 .

[61]  Geoffrey Blewitt,et al.  Crustal displacements due to continental water loading , 2001 .

[62]  W. Menke.  Geophysical data analysis : discrete inverse theory , 1984 .

[63]  Jan M. Johansson,et al.  BIFROST Project: Studies of Variations of Absolute Sea Level in Conjunction with the Postglacial Rebound of Fennoscandia , 2000 .

[64]  R. Preisendorfer,et al.  Principal Component Analysis in Meteorology and Oceanography , 1988 .

引用
Near real‐time monitoring of volcanic surface deformation from GPS measurements at Long Valley Caldera, California
2013
Parkfield earthquake: Stress-driven creep on a fault with spatially variable rate-and-state friction parameters
2009
Impacts of Local Effects and Surface Loads on the Common Mode Error Filtering in Continuous GPS Measurements in the Northwest of Yunnan Province, China
Sensors
2020
Creep and locking of a low‐angle normal fault: Insights from the Altotiberina fault in the Northern Apennines (Italy)
2016
Vertical GPS ground motion rates in the Euro‐Mediterranean region: New evidence of velocity gradients at different spatial scales along the Nubia‐Eurasia plate boundary
2013
A Combined Velocity Field of the Mediterranean Region
2017
Statistical Approaches to Detecting Transient Signals in GPS: Results from the 2009–2011 Transient Detection Exercise
2013
Detecting transient signals in geodetic time series using sparse estimation techniques
2014
Boundary-Included Enhanced Water Storage Changes Inferred by GPS in the Pacific Rim of the Western United States
Remote. Sens.
2020
Extracting the regional common‐mode component of GPS station position time series from dense continuous network
2016
Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations
Pure and Applied Geophysics
2018
A factor model approach for the joint segmentation with between‐series correlation
Scandinavian Journal of Statistics
2015
Characterizing noise in daily GPS position time series with overlapping Hadamard variance and maximum likelihood estimation
2017
Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series
Mathematical Geosciences
2018
Crustal motions in Great Britain: evidence from continuous GPS, absolute gravity and Holocene sea level data
2009
Modeling and Forecasting the GPS Zenith Troposphere Delay in West Antarctica Based on Different Blind Source Separation Methods and Deep Learning
Sensors
2020
Fault slip rates and interseismic deformation in the western Transverse Ranges, California
2013
New insights into active tectonics and seismogenic potential of the Italian Southern Alps from vertical geodetic velocities
Solid Earth
2020
Noise analysis for environmental loading effect on GPS time series
2016
Spatiotemporal Signal and Noise Analysis of GPS Position Time Series of the Permanent Stations in China
2014