Time Series Analysis and Its Applications: With R Examples

By Robert B. Lund. 2007

557 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/138b154a6192b549cacd7fec210843a6fe3c098f

Citations in the corpus

• Experimentally testing the role of foundation species in forests: the Harvard Forest Hemlock Removal Experiment (2010, unknown): “At one extreme, air and soil temperature data are recorded continuously and logged at 1 hour intervals (hourly means, minima, and maxima) and robust time-series analysis (Shumway & Stoffer 2006) of these data is already possible (Fig.”
• Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, unknown): “The simulations of the three benchmark models are implemented in a statistical software named R [49].”
• Identifying Platform Effects in Social Media Data (2016, unknown): “We apply both parametric and nonparametric models; for nonparametrics, we use local linear regression as is standard in regression discontinuity design (Imbens and Lemieux 2008) and is also appropriate for time series (Shumway and Stoffer 2011).”
• Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany (2017, unknown): “To estimate the effect of exogenous variables such as wind and solar power on a dependent variable of interest such as electricity price volatility, we use the seasonally adjusted autoregressive moving average (SARMA(p,q)(P,Q)[s]) model (Shumway and Stoffer, 2011):”
• Exploiting Social Relations and Sentiment for Stock Prediction (2014, unknown): “We build a two variables VAR model to predict the movement of a stock’s daily closing price.”
• Testing for Change Points in Time Series (2010, unknown): “In Shumway and Stoffer (2006), stationary time series models, such as AR(1) and MA(2), have been used to fit the data.”
• A test for second‐order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series (2013, unknown): “Figs 1 and 2 show our test being applied to the earthquake P- and the explosion P-wave data from Shumway and Stoffer (2006) respectively.”
• Space–time modelling of extreme events (2012, unknown): “Yi − bn/=an}i=1 converges to a non-homogeneous Poisson process on regions of the form [t1, t2] × [u, ∞/, with 0 <t1 <t2 < 1 (Leadbetter, 1991; Smith, 1989). In practice, the data often exhibit temporal dependence, and the aforementioned asymptotic results can be extended to stationary sequences with”
• Burrow‐dwelling ecosystem engineers provide thermal refugia throughout the landscape (2013, unknown): “This approach controls for autocorrelations within each data set with respect to time and temperature and thus tests the overall relationship between two temperature data sets while accounting for time lags in those relationships (Shumway & Stoffer, 2011).”
• Nested Sequential Monte Carlo Methods (2015, unknown): “The typical scenario that we consider is the well-known problem of inference in time series or state space models (Shumway & Stoffer, 2011; Capp ´ e et al., 2005).”
• The Gaussian Graphical Model in Cross-Sectional and Time-Series Data (2016, unknown): “The simplest way to deal with temporal ordering of cases is to incorporate the effect between consecutive measurements (Chatfield, 2016; Hamilton, 1994; Shumway & Stoffer, 2010).”