Financial model calibration using consistency hints

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market

An on-line harmonics elimination PWM scheme for three-phase voltage source inverters

An on-line harmonic elimination PWM (HEPWM) scheme for three-phase voltage source inverters is proposed. It is based on curve fitting method derived from the trajectories of the exact (off-line) HEPWM angles. The main advantage of the technique is its fast and efficient realization using a microprocessor. An outline to obtain the switching angles is presented. The method is proven by experimental result

A modal approach to hyper-redundant manipulator kinematics

This paper presents novel and efficient kinematic modeling techniques for “hyper-redundant” robots. This approach is based on a “backbone curve” that captures the robot's macroscopic geometric features. The inverse kinematic, or “hyper-redundancy resolution,” problem reduces to determining the time varying backbone curve behavior. To efficiently solve the inverse kinematics problem, the authors introduce a “modal” approach, in which a set of intrinsic backbone curve shape functions are restricted to a modal form. The singularities of the modal approach, modal non-degeneracy conditions, and modal switching are considered. For discretely segmented morphologies, the authors introduce “fitting” algorithms that determine the actuator displacements that cause the discrete manipulator to adhere to the backbone curve. These techniques are demonstrated with planar and spatial mechanism examples. They have also been implemented on a 30 degree-of-freedom robot prototype

Curve Fitting Simplified: Exploring the Intuitive Features of CurvPy

Curve fitting is a fundamental task in data analysis, allowing researchers to uncover underlying patterns and relationships in their datasets. In this paper, we introduce CurvPy, a powerful data analysis tool designed to streamline the curve-fitting process. CurvPy offers three main functionalities: DataSleuth, FuncPlot, and OptiFit. DataSleuth analyses input data in CSV format and provides a best-guess estimate of the underlying mathematical function. FuncPlot enables users to visually inspect the fit between the function and the data by generating graphs. OptiFit harnesses the power of optimal parameters, allowing effortless optimisation of equation parameters for precise and efficient data modelling. CurvPy is built using Flask, pandas, numpy, matplotlib, scipy, and scikit-learn, providing a user-friendly interface and efficient computational capabilities. By integrating these tools, CurvPy empowers researchers to gain insights from their data and will help to make decisions. Evaluation demonstrates the effectiveness and efficiency of CurvPy in diverse curve-fitting scenarios. The availability of CurvPy as an open-source tool further encourages collaboration and expands its potential applications in various domains. Overall, CurvPy offers a comprehensive solution for curve-fitting tasks and holds great promise for advancing data analysis techniques

Gaia Eclipsing Binary and Multiple Systems. A study of detectability and classification of eclipsing binaries with Gaia

In the new era of large-scale astronomical surveys, automated methods of analysis and classification of bulk data are a fundamental tool for fast and efficient production of deliverables. This becomes ever more imminent as we enter the Gaia era. We investigate the potential detectability of eclipsing binaries with Gaia using a data set of all Kepler eclipsing binaries sampled with Gaia cadence and folded with the Kepler period. The performance of fitting methods is evaluated with comparison to real Kepler data parameters and a classification scheme is proposed for the potentially detectable sources based on the geometry of the light curve fits. The polynomial chain (polyfit) and two-Gaussian models are used for light curve fitting of the data set. Classification is performed with a combination of the t-SNE (t-distrubuted Stochastic Neighbor Embedding) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. We find that approximately 68% of Kepler Eclipsing Binary sources are potentially detectable by Gaia when folded with the Kepler period and propose a classification scheme of the detectable sources based on the morphological type indicative of the light curve, with subclasses that reflect the properties of the fitted model (presence and visibility of eclipses, their width, depth, etc.).Comment: 9 pages, 18 figures, accepted for publication in Astronomy & Astrophysic

A two-component model for fitting light-curves of core-collapse supernovae

We present an improved version of a light curve model, which is able to estimate the physical properties of different types of core-collapse supernovae having double-peaked light curves, in a quick and efficient way. The model is based on a two-component configuration consisting of a dense, inner region and an extended, low-mass envelope. Using this configuration, we estimate the initial parameters of the progenitor via fitting the shape of the quasi-bolometric light curves of 10 SNe, including Type IIP and IIb events, with model light curves. In each case we compare the fitting results with available hydrodynamic calculations, and also match the derived expansion velocities with the observed ones. Furthermore, we also compare our calculations with hydrodynamic models derived by the SNEC code, and examine the uncertainties of the estimated physical parameters caused by the assumption of constant opacity and the inaccurate knowledge of the moment of explosion

Efficiency and Accuracy in Quadratic Curve Fitting: A Comparative Analysis of Optimization Techniques

In this paper, we investigate an optimization methods might be applied for solving curve fitting by making use of a quadratic model. To discover the ideal parameters for the quadratic model, synthetic experimental data is generated, and then two unique optimization approaches, namely differential evolution and the Nelder-Mead algorithm, are applied to the problem in order to find the optimal values for those parameters. The mean squared error as well as the correlation coefficient are both metrics that are incorporated into the objective function. When the results of these algorithms are compared, trade-offs between the rate of convergence and the quality of the fit are revealed. This work sheds light on the necessity of selecting proper optimization algorithms for specific circumstances and provides insights into the balance that must be struck between accurate curve fitting and efficient use of computational resources in the process of curve fitting