Modeling and forecasting interval time series with threshold models

Rodrigues, Paulo Maria; Salish, Nazarii

doi:10.1007/s11634-014-0170-x

Cited by 48 publications

(21 citation statements)

References 26 publications

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“…An interesting development is the use of the methods of this work for other control and monitoring issues related to wind turbine operation: for example, monitoring the effect of blade pitches re-alignment according to the technique proposed in [43], or monitoring the operation of the wind turbines [40]. Furthermore, a very promising direction of the studies about wind turbine power curve upgrades is the use of time-resolved data, having sampling time of the order of second: this kind of data have considerable potentiality for performance control and monitoring [44], but their time scale calls for more advanced time-series analysis [45].…”

Section: Discussionmentioning

confidence: 99%

Wind Turbine Power Curve Upgrades: Part II

Astolfi

Castellani

2019

Energies

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Wind turbine power upgrades have recently become a debated topic in wind energy research. Their assessment poses some challenges and calls for devoted techniques: some reasons are the stochastic nature of the wind and the multivariate dependency of wind turbine power. In this work, two test cases were studied. The former is the yaw management optimization on a 2 MW wind turbine; the latter is a comprehensive control upgrade (pitch, yaw, and cut-out) for 850 kW wind turbines. The upgrade impact was estimated by analyzing the difference between the post-upgrade power and a data-driven simulation of the power if the upgrade did not take place. Therefore, a reliable model for the pre-upgrade power of the wind turbines of interest was needed and, in this work, a principal component regression was employed. The yaw control optimization was shown to provide a 1.3% of production improvement and the control re-powering provided 2.5%. Another qualifying point was that, for the 850 kW wind turbine re-powering, the data quality was sufficient for an upgrade estimate based on power curve analysis and a good agreement with the model result was obtained. Summarizing, evidence of the profitability of wind turbine power upgrades was collected and data-driven methods were elaborated for power upgrade assessment and, in general, for wind turbine performance control and monitoring.

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Section: Discussionmentioning

confidence: 99%

Wind Turbine Power Curve Upgrades: Part II

Astolfi

Castellani

2019

Energies

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“…For every DGP, we generate 1000 samples and evaluate the performance of each estimation method according to: (i) Root Mean Squared Error for upper and lower bounds, (ii) Coverage (CR) and Efficiency Rates (ER) of the estimated intervals (Rodrigues and Salish 2011), (iii) Multivariate Loss Functions (MLF) for the vector of lower and upper bounds (Komunjer and Owyang 2011), and (iv) Mean Distance Error (MDE) between the fitted and actual intervals (Arroyo, González-Rivera, and Maté 2010).…”

Section: In-sample Evaluation Criteria : Loss Functionsmentioning

confidence: 99%

Constrained Regression for Interval-Valued Data

González‐Rivera

Wang

2013

Journal of Business & Economic Statistics

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Current regression models for interval-valued data do not guarantee that the predicted lower bound of the interval is always smaller than its upper bound. We propose a constrained regression model that preserves the natural order of the interval in all instances, either for in-sample fitted intervals or for interval forecasts. Within the framework of interval time series, we specify a general dynamic bivariate system for the upper and lower bounds of the intervals. By imposing the order of the interval bounds into the model, the bivariate probability density function of the errors becomes conditionally truncated. In this context, the ordinary least squares (OLS) estimators of the parameters of the system are inconsistent. Estimation by maximum likelihood is possible but it is computationally burdensome due to the nonlinearity of the estimator when there is truncation. We propose a two-step procedure that combines maximum likelihood and least squares estimation and a modified two-step procedure that combines maximum likelihood and minimum-distance estimation. In both instances, the estimators are consistent. However, when multicollinearity arises in the second step of the estimation, the modified two-step procedure is superior at identifying the model regardless of the severity of the truncation. Monte Carlo simulations show good finite sample properties of the proposed estimators. A comparison with the current methods in the literature shows that our proposed methods are superior by delivering smaller losses and better estimators (no bias and low mean squared errors) than those from competing approaches. We illustrate our approach with the daily interval of low/high SP500 returns and find that truncation is very severe during and after the financial crisis of 2008, so OLS estimates should not be trusted and a modified two-step procedure should be implemented. Supplementary materials for this article are available online.

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“…Second, interval data can also appear when dealing with big data sets to reduce dimensionality. For example, in stock markets, when intraday prices are available, one could analyze daily intervals of low/high asset prices; see, for example, Rodrigues and Salish (). One advantage of analyzing low/high stock price intervals instead of all available intraday returns is that problems related to irregular temporal spacing, strong diurnal patterns, microstructure noise or complex dependencies are avoided; see Russell and Engle () for the problems associated with high‐frequency data, and Alizadeh et al (), Brandt and Diebold (), and Shu and Zhang () for the robustness of the volatility estimator based on the range in the presence of microstructure noise such as bid–ask bounce.…”

Section: Introductionmentioning

confidence: 99%

Prediction regions for interval‐valued time series

González‐Rivera

Luo

Ruiz

2020

J of Applied Econometrics

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We approximate probabilistic forecasts for interval-valued time series by offering alternative approaches. After fitting a possibly non-Gaussian bivariate vector autoregression (VAR) model to the center/log-range system, we transform prediction regions (analytical and bootstrap) for this system into regions for center/range and upper/lower bounds systems. Monte Carlo simulations show that bootstrap methods are preferred according to several new metrics. For daily S&P 500 low/high returns, we build joint conditional prediction regions of the return level and volatility. We illustrate the usefulness of obtaining bootstrap forecasts regions for low/high returns by developing a trading strategy and showing its profitability when compared to using point forecasts.

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Modeling and forecasting interval time series with threshold models

Cited by 48 publications

References 26 publications

Wind Turbine Power Curve Upgrades: Part II

Wind Turbine Power Curve Upgrades: Part II

Constrained Regression for Interval-Valued Data

Prediction regions for interval‐valued time series

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