Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 27 for “"Singular spectrum analysis"”.
-
Bayesian singular spectrum analysis with state dependent models
The analysis of time series using Singular Spectrum Analysis has become an important area of statistics with application in a variety of fields such as economics, geophysics, engineering, medicine and many others. In fact, in this method there is no need to make any statistical assumptions such as …
-
Theoretical advancements and applications in singular spectrum analysis.
Singular Spectrum Analysis (SSA) is a nonparametric time series analysis and forecasting technique which has witnessed an augment in applications in the recent past. The increased application of SSA is closely associated with its superior filtering and signal extraction capabilities which also …
-
Reduction of wind induced microphone noise using singular spectrum analysis technique
… of wind noise. The new technique is based on the Singular Spectrum Analysis methodwhich has recently seen many successful paradigms in the separation of biomedical signals, e.g., separating heart soundfrom lung noise. It has also been successfully implemented to de-noise signals in various …
-
Change Point Detection in Time Series via Multivariate Singular Spectrum Analysis
… for CPD that combines a variant of multivariate singular spectrum analysis (mSSA) approach with the cumulative sum (CUSUM) procedure for sequential hypothesis testing. In particular, we model the underlying dynamics of multivariate time series observations through the spatio-temporal model …
-
Overlapped speech and music segmentation using singular spectrum analysis and random forests
… innovative method of audio decomposition using Singular Spectrum Analysis (SSA) that has been studied extensively and has received increasing attention in the past two decades as a time series decomposition method with many applications; adoption and development of driven classification methods; …
-
Multivariate Singular Spectrum Analysis: A Principled, Practical, and Performant Solution for Time Series Imputation and Forecasting
The analysis of multivariate time series data is of great interest across many domains, including cyber-physical systems, finance, retail, healthcare to name a few. A common goal across all of these domains is accurate imputation and forecasting of multivariate time series in the presence of noisy …
-
Developing Pattern and Anomaly Detection Methods in Influence Campaigns
… methods included moving average models and Singular Spectrum Analysis (SSA). Machine learning techniques included the use of an autoencoder and an LSTM neural network. These methods provide different ways to visualize and characterize the data. Together, the approaches offer a holistic …
-
An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications
… with particle techniques, which disturbs the analysis of the results. The uncertainty in the mean of the ensemble is due to fluctuations caused e.g. by additional forcing terms (thermostats). Extracting the genuine information from indirect, noisy measurements is analogous to solving the …
-
SAMoSSA: Multivariate Singular Spectrum Analysiswith Stochastic Autoregressive Noise
The well-established practice of time series analysis involves (i) estimating deterministic, non-stationary trend and seasonality components, followed by (ii) learning the residual stochastic, stationary components. Recently, it has been shown that one can learn the deterministic non-stationary …
-
A study of sub-orbital and millennial-scale climate variability over the past 1.4 million years in the Northern Atlantic
… methods have been employed including multi-taper analysis with different numbers of Slepian tapers, Singular Spectrum Analysis (SSA), noise background estimation methods (Mann and Lees, 1996), Akaike' s Information Criterion for AR(n) model fitting to a data set, and chi-square distribution …
-
Feature extraction and data reduction for hyperspectral remote sensing Earth observation
Earth observation and land-cover analysis became a reality in the last 2-3 decades thanks to NASA airborne and spacecrafts such as Landsat. Inclusion of Hyperspectral Imaging (HSI) technology in some of these platforms has made possible acquiring large data sets, with high potential in analytical …
-
Data Driven Prediction Without a Model
… wind system using Multi-channel Singular Spectrum Analysis. The Nearest Neighbor ETKF is applied to ensemble forecasts made using model data, constructed from long time series of the data from each magnetometer, in addition to observations in the reconstructed phase space, …
-
Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods
… equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. …
-
A novel statistical signal processing approach for analysing high volatile expression profiles.
… and signal processing algorithm based on Singular Spectrum Analysis (SSA) for extracting the signal of bicoid gene. Using the proposed SSA algorithm which is based on the minimum variance estimator, the extraction of bcd signal from its noisy profile is considerably improved compared to …
-
Simulating Dynamical Systems from Data
… employ variants of the classical multivariate singular spectrum analysis (mSSA) algorithm and establish a link between time series analysis and Matrix/Tensor Completion. Second, we develop and analyze an algorithm for change point detection inspired by the factorization structure and based on …
-
Advanced modelling and analytics for effective change and anomaly detection in hyperspectral images.
… lightweighted network, SSA-LHCD, combines the singular spectrum analysis (SSA) as a preprocessing step with a 2D self-attention module, further improving the detection accuracy while reducing the number of the hyperparameters of the model. Experimental results demonstrate that these two …
-
Time series estimation in a spiked signal regime
… state-of-the-art techniques in addition to an analysis of TSCC. These methods are used to solve the problem of estimating the state of dynamical system, with partial, noisy observations. Standard textbook techniques are not reliable in state estimation due to their inability to handle missing …
-
Aplicação da análise espectral singular à análise de risco
… muito poderosa conhecida como Análise Espectral Singular (SSA, do inglês Singular Spectrum Analysis). A SSA é uma técnica em que não é necessário conhecer o modelo paramétrico da série temporal, baseando-se apenas nos dados e pode ser aplicado em qualquer série com algum potencial de estrutura. …
-
An Overview on the performance of sprinters with lower limb amputation/impairment in Paralympic Games.
… exercise) based on the novel approach of Singular Spectrum Analysis (SSA). The results of forecast indicate that the Paralympians will enhance their performance in 2015 Paralympic Games.
-
Quasi-periodic oscillations in observed and simulated temperatures, and implications for the future
… models (GCMs). We employ a non-parametric Singular Spectrum Analysis (SSA) approach that enables the study of non-periodic – so-called “quasi-periodic” oscillations (QPOs). SSA can resolve QPOs that have periods up to half the length of the record, something which is impossible using …
Page 1 of 2