Peru Muniain, Florian Ziel
arXiv 19 Oct 2018 · Econometrics · publishedInternational Journal of Forecasting (2020) · 45 citations (OpenAlex)
arXiv:1810.08418 · PDF · DOI · OpenAlex · Extracted main text
In this paper we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price, hence we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by OLS and elastic net and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component on a mean reverting jump diffusion model in the residuals. The models' forecasts are evaluated using four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included or not. In the results it is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure in order to properly forecast correlated time series.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Uniejewski, B., Nowotarski, J., and Weron, R (2016) Automated variable selection and shrinkage for day-ahead electricity price forecasting | 1.000 | 5 | 3 | 100% |
| 2 | Weron, R (2014) Electricity price forecasting: A review of the state-of-the-art with a look into the future | 0.811 | 4 | 2 | 100% |
| 3 | Ziel, F. and Weron, R (2018) Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks self | 0.737 | 3 | 2 | 100% |
| 4 | Dai, B., Ding, S., Wahba, G., et al (2013) Multivariate bernoulli distribution | 0.644 | 2 | 2 | 100% |
| 5 | Gneiting, T. and Raftery, A. E (2007) Strictly proper scoring rules, prediction, and estimation | 0.644 | 2 | 2 | 100% |
| 6 | Keles, D., Genoese, M., Möst, D., and Fichtner, W (2012) Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices | 0.644 | 2 | 2 | 100% |
| 7 | Ziel, F (2016) Forecasting electricity spot prices using lasso: On capturing the autoregressive intraday structure self | 0.644 | 2 | 2 | 100% |
| 8 | Bollerslev, T (1990) Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model | 0.405 | 1 | 1 | 100% |
| 9 | Cartea, A. and Figueroa, M. G (2005) Pricing in electricity markets: a mean reverting jump diffusion model with seasonality | 0.405 | 1 | 1 | 100% |
| 10 | Diebold, F. X. and Mariano, R. S (2002) Comparing predictive accuracy | 0.405 | 1 | 1 | 100% |
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