Cubic Smoothing Spline Research Articles

Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms

The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. The data used in this study is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict the volatility of four cryptocurrencies, namely Bitcoin, Ethereum, XRP, and Tether, and compare their predictive power in terms of forecasting accuracy. The predictive capabilities of CSS, NNETAR, and GMDH-NN are compared and evaluated by mean absolute error (MAE) and root-mean-square error (RMSE). Regarding the return volatility of Bitcoin and XRP markets, the forecasted results remarkably suggest that in contrast to rival approaches, the CSS can be an effective model to boost the predicting accuracy in the sense that it has the lowest forecast errors. Considering the Ethereum markets’ volatility, the MAE and RMSE associated with NNETAR are smaller than the MAE and RMSE of CSS and GMDH-NN algorithm, which ensures the effectiveness of NNETAR as compared to competing approaches. Similarly, in case of Tether markets’ volatility, the corresponding MAE and RMSE reveal that the GMDH-NN algorithm is an efficient technique to enhance the forecasting performance. We notice that no single tool performed uniformly for all cryptocurrency markets. The policymakers can adopt the model for forecasting cryptocurrency volatility accordingly.

A Robust Procedure to Implement Dynamic Stall Models Into Actuator Line Methods for the Simulation of Vertical-Axis Wind Turbines

Abstract The actuator line method (ALM), combining a lumped-parameter representation of the rotating blades with the computational fluid dynamics (CFD) resolution of the turbine flow field, stands out among the modern simulation methods for wind turbines as probably the most interesting compromise between accuracy and computational cost. Being however a method relying on tabulated coefficients for modeling the blade-flow interaction, the correct implementation of the submodels to account for higher-order aerodynamic effects is pivotal. Inter alia, the introduction of a dynamic stall model is extremely challenging: first, it is important to extrapolate a correct value of the angle of attack (AoA) from the solved flow field; second, the AoA history needed to calculate the rate of dynamic variation of the angle itself is characterized by a low signal-to-noise ratio, leading to severe numerical oscillations of the solution. The study introduces a robust procedure to improve the quality of the AoA signal extracted from an ALM simulation. It combines a novel method for sampling the inflow velocity from the numerical flow field with a low-pass filtering of the corresponding AoA signal based on cubic spline smoothing (CSS). Such procedure has been implemented in the actuator line module developed by the authors for the commercial ansysfluent solver. To verify the reliability of the methodology, two-dimensional (2D) unsteady Reynolds-averaged Navier–Stokes (URANS) simulations of a test two-blade Darrieus H-rotor, for which high-fidelity experimental and numerical blade loading data were available, have been performed for a selected unstable operation point.

Instantaneous Rate of Ice Mass Changes in Antarctica Observed by Satellite Gravimetry

A method for computing instantaneous rate of the Antarctica ice sheet (AIS) mass changes is proposed in this letter. The method combines the ensemble empirical mode decomposition (EEMD) and cubic spline smoothing (CSS). First, EEMD is used to extract the nonlinear trend from the time series of the AIS mass changes observed by satellite gravimetry, and then CSS is used to fit the nonlinear trend. The first-order derivative from the fit curve is the instantaneous rate. Compared with the linear rate, the instantaneous rate improves the temporal resolution of mass change rate. The instantaneous rate reveals the complex behavior of the AIS mass changes; at regional scale, mass changes show negative rate for one period and positive rate for another. This important information is masked by the linear rate which only suggests an overall increasing or decreasing trend. The instantaneous rate promotes our understanding of the mass changes in the AIS and shows implication for detecting the anomalous changes in climate.

Comparisons between the Local Polynomial Kernel estimators and the Cubic Smoothing Splines

The term Nonparametric regression comes to signify the absence of parameters in the regression model. The scope of nonparametric regression is very broad nowadays, ranging from "smoothing" the relationship between two variables in a scatter plot to multiple-regression analysis. Extraordinary, only several years ago, methods of nonparametric regression analysis have been used widely because of advances in statistical techniques and related computing facilities, and are now a serious alternative to many traditional parametric regression modeling. This paper aims to provide using two methods of the nonparametric regression, namely; the Local Polynomial Kernel (LPK) estimator and Cubic Smoothing Splines (CSS) estimator with emphasis on the role of selecting the smoothing parameter (bandwidth) in each estimator. We have investigated the performance of these two smoothing estimators (LLK and CSS) under two different settings, fixed or random designs. This was achieved through conducting many simulation studies using four different example regression functions; two different distributions of errors term either normal or mixture, and different sample sizes. Two study the estimation of the derivative functions using those two smoothing estimators (Local Quadratic/Cubic Kernel and Cubic Smoothing Splines) in the process of smoothing the 1st / 2nd derivative functions respectively also been considered through conducting several simulation studies as well. Applications on real datasets also been considered. Lastly, the advantages and disadvantages of these two nonparametric regression estimators have been discussed.

Cubic smoothing splines background correction in on-line liquid chromatography–Fourier transform infrared spectrometry

A background correction method for the on-line coupling of gradient liquid chromatography and Fourier transform infrared spectrometry (LC–FTIR) is proposed. The developed approach applies univariate background correction to each variable (i.e. each wave number) individually. Spectra measured in the region before and after each peak cluster are used as knots to model the variation of the eluent absorption intensity with time using cubic smoothing splines (CSS) functions. The new approach has been successfully tested on simulated as well as on real data sets obtained from injections of standard mixtures of polyethylene glycols with four different molecular weights in methanol:water, 2-propanol:water and ethanol:water gradients ranging from 30 to 90, 10 to 25 and from 10 to 40% (v/v) of organic modifier, respectively. Calibration lines showed high linearity with coefficients of determination higher than 0.98 and limits of detection between 0.4 and 1.4, 0.9 and 1.8, and 1.1 and 2.7 mg mL −1 in methanol:water, 2-propanol:water and ethanol:water, respectively. Furthermore the method performance has been compared with a univariate background correction approach based on the use of a reference spectra matrix (UBC-RSM) to discuss the potential as well as pitfalls and drawbacks of the proposed approach. This method works without previous variable selection and provides minimal user-interaction, thus increasing drastically the feasibility of on-line coupling of gradient LC–FTIR.

PEMODELAN KURVA IMBAL HASIL OBLIGASI KORPORASI RATING AA DAN A DENGAN NELSON SIEGEL SVENSSON DAN CUBIC SPLINE SMOOTHING

Bond is one of commercial instrument that influence economic sector in Indonesia. Bond transaction can't be made in the market directly, but it has been traded through securities. On average, there are only few bond transactions with the various market prices. Benchmarking is, therefore, needed to be created to determining bond price through yield curve. Through yield curve, the relation between yield of bond with same credit risk (rating) and different time to maturity can be seen. This research is conducted by employing time to maturity to model the yield of some selected corporate bonds with rating of AA and A. Two methods, Nelson Siegel Svensson (NSS) method couple with Levenberg-Marquardt optimization and Cubic Spline Smoothing (CSS) are employed here. These two methods have been applied to data from Indonesian Stock Exchange (IDX) ranging September to November 2008. The results show that CSS give smallest RMSE and MAE. In contrast, Nelson Siegel Svensson reports a model which more parsimony, more easily to be explained, and more adaptable to keep upfoward maturity than CSS. This research takes into account that NSS is better to be to model corporate bonds yield curve than CSS. Another importance conclusion that can be gather is that corporate bonds (with rating of AA and A) yield are hang about under IGSYC in certain period. Its means, that corporate bond market in Indonesia is not good for investment comparing to government bond.

GPS 기준국망을 이용한 전리층 총전자수 변화 검출 연구

강한 지자기 폭풍이 발생하였을 때 한반도 상공의 전리층내 총전자수(TEC, Total Electron Contents) 변화를 분석하기 위해 GPS 기준국망을 데이터를 이용한 지역적인 전리층 감시모델을 개발하였다. 전리층 총전자수 감시모델 개발을 위해 한국천문연구원에서 운용중인 대전 IGS(International GNSS Service) 기준국을 포함한 전국에 고르게 분포하고 있는 9개의 GPS 기준국 데이터를 이용하였다. 또한 순간적인 전리층 변화 특성을 분석하기 위해 CSS(Cubic Spline Smoothing)기법을 적용하였고, 그 결과 2003년 11월 20일 강한 지자기 폭풍이 발생했을 때 한반도 상공에서 총전자수의 순간적인 변화를 검출할 수 있었다. 이때에는 평일과 비교했을 때 특정시각의 약 1.5배 이상 총전자수가 증가함을 보였다. 마지막으로 지구 자기장 활동 정도를 나타내는 Kp 지수, Dst 지수 그리고 천문연 GPS 기준국망 데이터를 이용해 산출된 총전자수 변화와의 연관성을 제시했다. We established a regional ionospheric model for investigating ionospheric TEC (Total Electron Contents) variations over the Korean Peninsula during major geomagnetic storms. In order to monitor the ionospheric TEC variations, we used nine permanent GPS reference stations uniformly distributed in South Korea operated by the Korea Astronomy and Space Science Institute (KASI). The cubic spline smoothing (CSS) interpolation method was used to analyze the characteristics of the ionospheric TEC variations. It has been found that variations of TEC over the Korean Peninsula increase when a major geomagnetic storm occurred on November 20, 2003. The TEC has increased about one and a half of those averaged quite days at the specific time during a geomagnetic storm. It has been indicated that the KASI GPS-derived TEC has a correlation with the geomagnetic storm indices (eq. Kp and Dst indices).

Global and local statistical properties of fixed-length nonparametric smoothers

The main purpose of this study is to analyze the global and local statistical properties of nonparametric smoothers subject to a priori fixed length restriction. In order to do so, we introduce a set of local statistical measures based on their weighting system shapes and weight values. In this way, the local statistical measures of bias, variance and mean square error are intrinsic to the smoothers and independent of the data to which they will be applied on. One major advantage of the statistical measures relative to the classical spectral ones is their easiness of calculation. However, in this paper we use both in a complementary manner. The smoothers studied are based on two broad classes of weighting generating functions, local polynomials and probability distributions. We consider within the first class, the locally weighted regression smoother (loess) of degree 1 and 2 (L1 and L2), the cubic smoothing spline (CSS), and the Henderson smoothing linear filter (H); and in the second class, the Gaussian kernel (GK). The weighting systems of these estimators depend on a smoothing parameter that traditionally, is estimated by means of data dependent optimization criteria. However, by imposing to all of them the condition of an equal number of weights, it will be shown that some of their optimal statistical properties are no longer valid. Without any loss of generality, the analysis is carried out for 13- and 9-term lengths because these are the most often selected for the Henderson filters in the context of monthly time series decomposition.

Predictive performance of some nonparametric linear and nonl-inear smoothers for noisy data

The purpose of this study is to discuss the weighting systems of several linear and non linear smoothers and to evaluate their predictive performances when applied to noisy time series. On this regard, we illustrate with three Canadian leading indicators which are representative of larger sets of time series characterised by a low, medium and high signal to noise ratio. The smoothers discussed are: (a) loess (a locally weighted regression smoother), (b) Gaussian Kernel smoother, (c) supersmoother, (d) cubic smoothing spline and (e) Dagum’s modified 13-term Henderson filter. Their performances are evaluated on the basis of three following main criteria for current economic analysis: (1) number of unwanted ripples or false turning points in the final estimated trend, (2) time lag in detecting ‘true’ turning points and (3) size of total revision of the concurrent trend estimates.

Smoothing methods for short-term trend analysis: cubic splines and Henderson filters

This study compares the smoothing properties of cubic splines functions with a very well known short-term trend estimator, the 13-term Henderson filter, and a new procedure developed by Dagum (1996) for the analysis of current economic conditions. The cubic smoothing spline (CSS) trend-cycles are estimated in two ways, namely, (I) using the smoothing parameter obtained with the generalized cross validation criterion and (II) imposing a fixed value found to fit well streches of volatile data present in the series. The comparison, done with a sample of Italian and Canadian series, is based on the number of unwanted ripples and time lag to detect a true turning point. The results are illustrated with four typical cases.