Least-squares polynomial approximation for short-term generation unit asset valuation

2009 ◽  
Author(s):  
N. S. Sisworahardjo ◽  
A. A. El-Keib ◽  
M. S. Alam
Keyword(s):  
Least Squares ◽  
Polynomial Approximation ◽  
Asset Valuation ◽  
Short Term ◽  
Generation Unit

scholarly journals Zastosowanie modeli wyceny aktywów CAPM, market timing i DLM do oceny inwestycji w spółki innowacyjne

Ekonomia ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 109-124
Author(s):  
Agnieszka Panek
Keyword(s):  
Least Squares ◽  
Dynamic Models ◽  
Market Timing ◽  
Distributed Delays ◽  
Asset Valuation ◽  
Short Term ◽  
Rates Of Return ◽  
Timing Model ◽  
The Impact

The aim of this article is to verify whether investment innovation managers, due to their specificity, should in particular have the ability to sense short-term trends and how this affects the effectiveness and risk of such investments.Empirical research was based on the use of asset valuation models of the classic CAPM, MT (market timing) model, and DLM (dynamic models with distributed delays), the parameters of which were estimated using the Classical Least Squares Methods, based on logarithmic rates of return of companies listed on the WSE in the period from 1st February 2015 to 2nd February 2020.The significantly negative value of the MNK — the estimator of parameter of the MT model, means that managers do not have the ability to sense short-term changes in the market regardless of the sector in which they operate. Furthermore, the impact of delays on market rates of return has been observed. The presented results may constitute recommendations for managers in terms of valuation of MT’s assets and skills and their impact on the effectiveness and risk of innovative investments.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

scholarly journals The Impact of Economic Growth, Trade Openness and Technological Progress on Renewable Energy Use in Organization for Economic Co-Operation and Development Countries

2021 ◽  
Author(s):  
Md. Mahmudul Alam ◽  
Wahid Murad
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Least Squares ◽  
Technological Progress ◽  
Energy Use ◽  
Trade Openness ◽  
Oecd Countries ◽  
Ordinary Least Squares ◽  
Short Term

This study investigates the short-term and long-term impacts of economic growth, trade openness and technological progress on renewable energy use in Organization for Economic Co-operation and Development (OECD) countries. Based on a panel data set of 25 OECD countries for 43 years, we used the autoregressive distributed lag (ARDL) approach and the related intermediate estimators, including pooled mean group (PMG), mean group (MG) and dynamic fixed effect (DFE) to achieve the objective. The estimated ARDL model has also been checked for robustness using the two substitute single equation estimators, these being the dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS). Empirical results reveal that economic growth, trade openness and technological progress significantly influence renewable energy use over the long-term in OECD countries. While the long-term nature of dynamics of the variables is found to be similar across 25 OECD countries, their short-term dynamics are found to be mixed in nature. This is attributed to varying levels of trade openness and technological progress in OECD countries. Since this is a pioneer study that investigates the issue, the findings are completely new and they make a significant contribution to renewable energy literature as well as relevant policy development.

Tensor alternating least squares grey model and its application to short-term traffic flows

2020 ◽  
Vol 89 ◽  
pp. 106145 ◽  
Author(s):  
Huiming Duan ◽  
Xinping Xiao ◽  
Jie Long ◽  
Yongzhi Liu
Keyword(s):  
Least Squares ◽  
Grey Model ◽  
Alternating Least Squares ◽  
Traffic Flows ◽  
Short Term

scholarly journals Multilevel weighted least squares polynomial approximation

2020 ◽  
Vol 54 (2) ◽  
pp. 649-677 ◽  
Author(s):  
Abdul-Lateef Haji-Ali ◽  
Fabio Nobile ◽  
Raúl Tempone ◽  
Sören Wolfers
Keyword(s):  
Least Squares ◽  
Polynomial Approximation ◽  
Weighted Least Squares ◽  
A Priori ◽  
Computational Cost ◽  
Discretization Error ◽  
Logarithmic Factor ◽  
Complexity Bounds ◽  
Single Level ◽  
Random Samples

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi-optimal approximation in a given polynomial subspace scales, up to a logarithmic factor, linearly in the dimension of this space. However, in many applications, the computation of samples includes a numerical discretization error. Thus, obtaining polynomial approximations with a single level method can become prohibitively expensive, as it requires a sufficiently large number of samples, each computed with a sufficiently small discretization error. As a solution to this problem, we propose a multilevel method that utilizes samples computed with different accuracies and is able to match the accuracy of single-level approximations with reduced computational cost. We derive complexity bounds under certain assumptions about polynomial approximability and sample work. Furthermore, we propose an adaptive algorithm for situations where such assumptions cannot be verified a priori. Finally, we provide an efficient algorithm for the sampling from optimal distributions and an analysis of computationally favorable alternative distributions. Numerical experiments underscore the practical applicability of our method.

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