Many processes in atmosphere-ocean science develop multiscale temporal and spatial patterns, with complex underlying dynamics and time-dependent external forcings. Because of the possible advances in our understanding and prediction of climate phenomena, extracting that variability empirically from incomplete observations is a problem of wide contemporary interest. Here, we present a technique ...

[1] Using nonlinear dynamical techniques, we statistically investigate whether the simulated substorms from global magnetohydrodynamic (MHD) models have a combination of global and multiscale features, revealed in substorm dynamics by Sitnov et al. [2000] and featured the phase transition-like behavior. We simulate seven intervals of total duration of 280 hours from the data set used in the abo...

Many processes in science and engineering develop multiscale temporal and spatial patterns, with complex underlying dynamics and time-dependent external forcings. Because of the importance in understanding and predicting these phenomena, extracting the salient modes of variability empirically from incomplete observations is a problem of wide contemporary interest. Here, we present a technique f...

Paper aims To predict monthly corn, soybean, and sugar spot prices in Brazil using hybrid forecasting techniques. Originality This study combines the Singular Spectrum Analysis with different methods. Research method paper presents a set of approaches combining (SSA) univariate time series methods, ranging from complex seasonality methods to machine learning autoregressive models Brazil. We car...

Smart factories and big data are important factors in the Fourth Industrial Revolution. aim for automation integration; however, most part is application of data. Despite extensive research on maintenance quality management data-based production equipment, industrial gathered analysis contain more normal than abnormal In addition, a significant amount energy expended pre-processing process to a...

A low-cost inertial navigation system (INS) and global satellite (GNSS) fusion position estimator is affected by accuracy limitations multiple noises, leading to significant errors in positioning estimation. This paper proposes a new algorithm, SSA-ESKF, which combines singular spectrum analysis (SSA) an error-state Kalman filter (ESKF). The measurement unit (IMU) GNSS data obtained from the re...

To reduce the impact of volatility on photovoltaic (PV) power generation forecasting and achieve improved accuracy, this article provides an in-depth analysis characteristics PV outputs under typical weather conditions. The trend similarity between simultaneous are found, a hybrid prediction model based feature matching, singular spectrum (SSA) long short-term memory (LSTM) network is proposed....

The characteristics of hydrological data include nonconsistency and nonlinearity. prediction accuracy can be improved through the combination both decomposition algorithm runoff model. Previous studies have typically focused on a single These compared before after decomposition, ignoring role multiple algorithms models. Considering limitations previous combinations models, this study will explo...