Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR–ARCH type processes

Ziel, Florian

doi:10.1016/j.csda.2015.11.016

Cited by 21 publications

(18 citation statements)

References 35 publications

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“…Here, iterative reweighting schemes can help to incorporate variance changing effects, especially for univariate approaches (see e.g. Ziel et al, 2015a;Ziel, 2016b).…”

Section: Conclusion and Guidelines For Energy Forecastersmentioning

confidence: 99%

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

2018

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We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate. We provide evidence that despite a minor edge in predictive performance overall, the multivariate modeling framework does not uniformly outperform the univariate one across all 12 considered datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This is an indication that combining advanced structures or the corresponding forecasts from both modeling approaches can bring a further improvement in forecasting accuracy. We show that this indeed can be the case, even for a simple averaging scheme involving only two models. Finally, we also analyze variable selection for the best performing high-dimensional lasso-type models, thus provide guidelines to structuring better performing forecasting model designs.or a univariate framework, the latter generally perform better for the first half of the day, whereas the former are better in the second half of the day. However, there has been no through, empirical study to date, involving many fine-tuned specifications from both groups. With this paper we want to fill the gap and provide much needed evidence. In particular we want to address three pertinent questions:1. Which modeling frameworkmultivariate or univariate -is better for EPF? 2. If one of them is better, is it better across all hours, seasons of the year and markets? 3. How many and which past values of the spot price process should be used in EPF models?The remainder of the paper is structured as follows. In Section 2 we thoroughly discuss the univariate and multivariate modeling frameworks, which are driven by different data-format perspectives. This is a crucial, conceptual part of the paper, which sets ground for the empirical analysis in the following Sections. In Section 3 we briefly describe the 12 price series used and present the area hyperbolic sine transform for stabilizing the variance of spot price data. In Section 4 we define 10 forecasting models representing eight model classes: (C1) the mean values of the past prices, (C2) similar-day techniques, (C3) sets of 24 parsimonious, interrelated autoregressive (AR) structures (so-called expert models), (C4) sets of 24 univariate AR models, (C5) vector autoregressive (VAR) models, (C6) sets of 24 parameter-rich, interrelated AR models estimated using the least absolute shrinkage and selection operator (i.e., lasso or LASSO; which shrinks to zero the coefficients of redundant explanatory variables), (C7) univariate AR models and (C8) univariate, parameter-rich AR models estimated using the lasso. In Section 5 we evaluate their performance on the basis of the Mean Absolute Error (MAE), the mean percentage deviation from the best (m.p.d.f.b.) model and using two variants of the Diebold and Mariano (1995) test for significant differences in the forecasting performance. We also discuss variable selection for the best performin...

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“…Here, iterative reweighting schemes can help to incorporate variance changing effects, especially for univariate approaches (see e.g. Ziel et al, 2015a;Ziel, 2016b).…”

Section: Conclusion and Guidelines For Energy Forecastersmentioning

confidence: 99%

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

2018

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“…In 1996, Tibshirani [42] proposed the least absolute shrinkage and selection operator (i.e., lasso or LASSO) that overcomes this disadvantage. It is the only shrinkage procedure that has been applied in EPF to a larger extent, however only in the last two years [7,9,18,25,43].…”

Section: Lasso and Elastic Netsmentioning

confidence: 99%

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

2016

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Abstract:In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall.

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“…The heteroscedasticity of residuals should be taken into account when designing the model. Ziel et al (2015) and Ziel (2015) suggest an iteratively reweighted lasso approach incorporating the volatility of the residuals. Their results suggest a significant improvement of the forecasting results.…”

Section: Discussionmentioning

confidence: 99%

Lasso estimation for GEFCom2014 probabilistic electric load forecasting

Ziel

Liu

2016

International Journal of Forecasting

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We present a lasso (least absolute shrinkage and selection operator) estimation based methodology for probabilistic load forecasting. The considered model can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach directly incorporates the temperature effects and reflects daily, weekly, and annual seasonal patterns and public holiday effects. We provide two empirical studies, one based on the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014 (GEFCom2014-L), and the other based on another recent probabilistic load forecasting competition that follows the similar setup as GEFCom2014-L. In both empirical case studies, the proposed methodology outperforms two multiple linear regression based benchmarks from a top 8 entry of GEFCom2014-L.

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Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR–ARCH type processes

Cited by 21 publications

References 35 publications

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

Lasso estimation for GEFCom2014 probabilistic electric load forecasting

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