Modeling Financial Time Series With S—Plus

By T. Burr. Technometrics, 2007

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A decoder-only foundation model for time-series forecasting (2023, methodology): “Such models include the classical VAR model [ZW06] as well as deep learning models like [SYD19, ZMW+22, ZZP+21] to name a few.”
Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021, unknown): “Although VAR models both spatial and temporal correlations among multiple time series, its representation ability to capture nonlinear and dynamic spatial-temporal correlations is weak.”
Exploring Granger causality between global average observed time series of carbon dioxide and temperature (2011, methodology): “a lag with sufficient information content on the two variables without over fitting (Zivot and Wang 2002).”
Effectiveness of copula-extreme value theory in estimating value-at-risk: empirical evidence from Asian emerging markets (2012, background): “Taiwan and Thailand have significant fat tails on both upper and lower sides of the change in foreign exchange rate distribution, while Indonesia, Malaysia, and Singapore only have significant fat tails on right side but not on left side.”
Time series interpolation via global optimization of moments fitting (2013, unknown): “[4,20,38,37], to sinc interpolation or wavelets (in connection to filtering and signal processing), see [35] and the references given there, and Chapter 19 in [34] for a detailed description of such methods.”