Nadja Klein, Michael Stanley Smith, David J. Nott
arXiv 5 Oct 2020 · Statistics — Methodology · publishedJournal of Applied Econometrics (2023) · 5 citations (OpenAlex)
arXiv:2010.01844 · PDF · DOI · OpenAlex · Extracted main text
Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. We propose two approaches to constructing deep time series probabilistic models based on a variant of RNN called an echo state network (ESN). The first is where the output layer of the ESN has stochastic disturbances and a shrinkage prior for additional regularization. The second approach employs the implicit copula of an ESN with Gaussian disturbances, which is a deep copula process on the feature space. Combining this copula with a non-parametrically estimated marginal distribution produces a deep distributional time series model. The resulting probabilistic forecasts are deep functions of the feature vector and also marginally calibrated. In both approaches, Bayesian Markov chain Monte Carlo methods are used to estimate the models and compute forecasts. The proposed models are suitable for the complex task of forecasting intraday electricity prices. Using data from the Australian National Electricity Market, we show that our deep time series models provide accurate short term probabilistic price forecasts, with the copula model dominating. Moreover, the models provide a flexible framework for incorporating probabilistic forecasts of electricity demand as additional features, which increases upper tail forecast accuracy from the copula model significantly.
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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 | McDermott, P. L. and Wikle, C. K (2017) An ensemble quadratic echo state network for non-linear spatio-temporal forecasting | 1.000 | 8 | 3 | 100% |
| 2 | Panagiotelis, A. and Smith, M (2008) Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions self | 1.000 | 6 | 3 | 100% |
| 3 | Ignatieva, K. and Trück, S (2016) Modeling spot price dependence in Australian electricity markets with applications to risk management | 1.000 | 5 | 4 | 100% |
| 4 | Gneiting, T., Balabdaoui, F., and Raftery, A. E (2007) Probabilistic forecasts, calibration and sharpness | 1.000 | 5 | 3 | 100% |
| 5 | Manner, H., Fard, F. A., Pourkhanali, A., and Tafakori, L (2019) Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae | 1.000 | 5 | 3 | 100% |
| 6 | Smith, M. S. and Shively, T. S (2018) Econometric modeling of regional electricity spot prices in the Australian market self | 1.000 | 5 | 3 | 100% |
| 7 | Serinaldi, F (2011) Distributional modeling and short-term forecasting of electricity prices by generalized additive models for location, scale and… | 0.928 | 4 | 3 | 100% |
| 8 | Gianfreda, A. and Bunn, D (2018) A stochastic latent moment model for electricity price formation | 0.843 | 3 | 3 | 100% |
| 9 | McDermott, P. L. and Wikle, C. K (2019) Deep echo state networks with uncertainty quantification for spatio-temporal forecasting | 0.843 | 3 | 3 | 100% |
| 10 | Nowotarski, J. and Weron, R (2018) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.843 | 3 | 3 | 100% |
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