Periodic Time Series Models

By Robert B. Lund. 2005

109 citations recorded by the source corpus.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/105e0f9ceefabb5a3ab7654e25bda3e80e2b4031

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Signal Processing

1. James W. Taylor
2. Patrick E. McSharry
3. Abdelhakim Aknouche
4. Abdelouahab Bibi

Citations in the corpus

• Quasi‐maximum likelihood estimation of periodic GARCH and periodic ARMA‐GARCH processes (2009, background): “…useful and appropriate for modelling many time series encountered in practice, which are characterized by a stochastic conditional variance with periodic dynamics (see also Franses and Paap, 2000, 2004; Ghysels and Osborn, 2001; Taylor, 2004, 2006; Martens et al., 2002, and the references therein).”
• Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, unknown): “A similar result was obtained by Garcia et al. (2005) who studied ARIMA models with GARCH residuals and concluded that ARIMA-GARCH outperforms a generic ARIMA model, but only when high volatility and price spikes are present.”
• Short-Term Load Forecasting Methods: An Evaluation Based on European Data (2007, background): “In these models, the parameters are allowed to change with the seasons [14].”