In this paper, a data-driven modeling technique is proposed for temperature forecasting. Due to the high dimensionality, LASSO is used as feature selection approach. Considering spatio-temporal structure of the weather dataset, first LASSO is applied in a spatial and temporal scenario, independently. Next, a feature is included in the model if it is selected by both. Finally, Least Squares Supp...

Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, ther...

How to analyze the features of stock price accurately and master the regularity of stock price changing with time quickly and effectively is of great theoretical and realistic significance and is an important research direction in financial field. For complicated non-linear and periodic variations of stock prices, a parallel computing model is proposed in this paper based on stock prediction al...

A quantitative structure–property relationship (QSPR) study was performed for predicting the hydrophobicity of Pt(IV) complexes. Two four-parameter equations, one based solely on structural descriptors derived from electrostatic potentials (ESPs) molecular surface, and other integrated ESP with surface area (AS), were firstly constructed. Mechanistic interpretations introduced elucidated in ter...

Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instabili...

The present study proposes a low-energy consumption multipoint sampler carried by deep-sea landing vehicle (DSLV) to meet the requirements of time series sampling in local areas and location wide areas, an optimization method structure based on least-squares support-vector machine (LSSVM) surrogate model multi-objective particle swarm (MOPSO) algorithm. First, overall core components, such as s...

The fast and accurate classification of surrounding rock mass is the basis for tunnel design construction has significant value in engineering applications. Therefore, this paper proposes a method classifying predicting based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). premise research that data acquired from digital drilling technology are divided into tr...

a r t i c l e i n f o Keywords: Data-driven model Model update Least squares support vector machine NOx emissions Coal-fired boiler This paper presents an adaptive least squares support vector machine (LSSVM) model with a novel update to tackle process variations. The key idea of the update is to divide the process variations into two main categories, namely, irreversible and reversible variati...

   In this paper, a new robust approach based on Least Square Support Vector Machine (LSSVM) as a proxy model is used for an automatic fractured reservoir history matching. The proxy model is made to model the history match objective function (mismatch values) based on the history data of the field. This model is then used to minimize the objective function through Particle Swarm Optimization (...