Time series analysis and its applications

By 竹安数博, 樋口友紀, 石井康夫, 豊田丈輔. 2007

1,088 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6f1f8d97fe730abfc5237c2876b641c32622e6ef

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Citations in the corpus

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models (2006, background): “Linear Gaussian State-Space Models (LGSSMs) 1 are fundamental in time-series analysis [1, 2, 3].”
Acoustic Emission Sensor Calibration for Absolute Source Measurements (2012, methodology): “Instead, we approximate the sensor response with a time domain model (see for example [27]), and use u3(x, t) and s(x, t) as the input and output, respectively, to estimate the model parameters.”
Study of cross-correlation in a self-affine time series of taxi accidents (2011, background): “These time series can be cross-correlated, as in [15,16] or [17–20], and in this case there are differentmethods for cross-correlation analysis [21–31].”
Analysing Ecological Data (2007, methodology): “Recently, multivariate extensions of these models have been developed and are called vector AR models (Shumway and Stoffer 2000; Liitkepohl 1991).”
Nonlinear impacts of climatic variability on the density‐dependent regulation of an insect vector of disease (2012, methodology): “Descriptive time series analysis We examined the correlation structure of mosquito time series data using standard techniques for time series analysis (Shumway & Stoffer, 2000).”
Multi-View Video Summarization (2010, background): “…is to approximate them with the Gaussian tribution (3) (4) where is the covariance matrix of , and is the one of . are normalized by (5) By virtual of nonlinear time series analysis [41] , the Gaussian entropy of shot is finally expressed as (6) where is the th element in the diagonal of matrix .”
Editorial: Special issue on time series analysis in the biological sciences (2012, methodology): “These data are taken from Shumway and Stoffer (2011) and are monthly growth rate of pneumonia and influenza deaths in the United States for 11 years, 1968–1978.”
Robust maximum-likelihood estimation of multivariable dynamic systems (2005, background): “The expressions (35) and (42) are established in Property P4.3 of [ 40 ].”
Robust stochastic optimization for reservoir operation (2015, methodology): “Statistical analysis [Shumway and Stoffer, 2010] shows that the inflows to TGR is best described by an (autoregressive integrated moving average) ARIMAð4; 1; 4Þ3ð1; 1; 1Þ36 model, where (4, 1, 4) are the nonseasonal parameters: the order of the autoregressive part is 4, the order of integrated part…”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, methodology): “A time series analysis [91, 92] includes techniques from both statistics and signal processing.”
Mean shift testing in correlated data (2011, methodology): “A cross-correlation analysis reveals that SOI tends to lead recruitment by half a year (see Shumway and Stoffer, 2006 for details).”
Reparameterization Gradient for Non-differentiable Models (2018, background): “• influenza [32] is a model for the US inﬂuenza mortality data in 1969.”