PSO-MISMO modeling strategy for multistep-ahead time series prediction

Dynamic Hybrid Model to Forecast the Spread of COVID-19 Using LSTM and Behavioral Models Under Uncertainty

To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.

Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete

Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices.

Hybrid Regularized Echo State Network for Multivariate Chaotic Time Series Prediction

Multivariate chaotic time series prediction is a hot research topic, the goal of which is to predict the future of the time series based on past observations. Echo state networks (ESNs) have recently been widely used in time series prediction, but there may be an ill-posed problem for a large number of unknown output weights. To solve this problem, we propose a hybrid regularized ESN, which employs a sparse regression with the L₁/2 regularization and the L₂ regularization to compute the output weights. The L₁/2 penalty shows many attractive properties, such as unbiasedness and sparsity. The L₂ penalty presents appealing ability on shrinking the amplitude of the output weights. After the output weights are calculated, the input weights, internal weights, and output weights are fine-tuning by a Hessian-free optimization method-conjugate gradient backpropagation algorithm. The fine-tuning helps to bubble up the input information toward the output layer. Besides, the largest Lyapunov exponent is used to calculate the predictable horizon of a chaotic time series. Experimental results on benchmark and real-world datasets show that our proposed method is superior to other ESN-based models, as sparser, smaller-absolute-value, and more informative output weights are obtained. All of the predictions within the predictable horizon of the proposed model are accurate.

Spatio-Temporal Interpolated Echo State Network for Meteorological Series Prediction

Spatio-temporal series prediction has attracted increasing attention in the field of meteorology in recent years. The spatial and temporal joint effect makes predictions challenging. Most of the existing spatio-temporal prediction models are computationally complicated. To develop an accurate but easy-to-implement spatio-temporal prediction model, this paper designs a novel spatio-temporal prediction model based on echo state networks. For real-world observed meteorological data with randomness and large changes, we use a cubic spline method to bridge the gaps between the neighboring points, which results in a pleasingly smooth series. The interpolated series is later input into the spatio-temporal echo state networks, in which the spatial coefficients are computed by the elastic-net algorithm. This approach offers automatic selection and continuous shrinkage of the spatial variables. The proposed model provides an intuitive but effective approach to address the interaction of spatial and temporal effects. To demonstrate the practicality of the proposed model, we apply it to predict two real-world datasets: monthly precipitation series and daily air quality index series. Experimental results demonstrate that the proposed model achieves a normalized root-mean-square error of approximately 0.250 on both datasets. Similar results are achieved on the long short-term memory model, but the computation time of our proposed model is considerably shorter. It can be inferred that our proposed neural network model has advantages on predicting meteorological series over other models.

Nonuniform State Space Reconstruction for Multivariate Chaotic Time Series

State space reconstruction is the foundation of chaotic system modeling. Selection of reconstructed variables is essential to the analysis and prediction of multivariate chaotic time series. As most existing state space reconstruction theorems deal with univariate time series, we have presented a novel nonuniform state space reconstruction method using information criterion for multivariate chaotic time series. We derived a new criterion based on low dimensional approximation of joint mutual information for time delay selection, which can be solved efficiently through the use of an intelligent optimization algorithm with low computation complexity. The embedding dimension is determined by conditional entropy, after which the reconstructed variables have relatively strong independence and low redundancy. The scheme, which integrates nonuniform embedding and feature selection, results in better reconstructions for multivariate chaotic systems. Moreover, the proposed nonuniform state space reconstruction method shows good performance in forecasting benchmark and actual multivariate chaotic time series.

Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load

This paper proposes an active power control method for supporting grid frequency regulation in wind farms (WF) considering improved fatigue load sensitivity of wind turbines (WT). The control method is concluded into two parts: frequency adjustment control (FAC) and power reference dispatch (PRD). On one hand, the proposed Fuzzy-PID control method can actively maintain the balance between power generation and grid load, by which the grid frequency is regulated when plenty of winds are available. The fast power response can be provided and frequency error can be reduced by the proposed method. On the other hand, the sensitivity of the WT fatigue loads to the power references is improved. The explicit analytical equations of the fatigue load sensitivity are re-derived to improve calculation accuracy. In the process of the optimization dispatch, the re-defined fatigue load sensitivity will be used to minimize fatigue load. Case studies were conducted with a WF under different grid loads and turbulent wind with different intensities. By comparing the frequency response of the WF, rainflow cycle, and Damage Equivalent Load (DEL) of the WT, the efficacy of the proposed method is verified.

Multivariate Chaotic Time Series Online Prediction Based on Improved Kernel Recursive Least Squares Algorithm

Kernel recursive least squares (KRLS) is a kind of kernel methods, which has attracted wide attention in the research of time series online prediction. It has low computational complexity and updates in a recursive form. However, as data size increases, computational complexity of calculating kernel inverse matrix will raise. And it has some difficulties in accommodating time-varying environments. Therefore, we have presented an improved KRLS algorithm for multivariate chaotic time series online prediction. Approximate linear dependency, dynamic adjustment, and coherence criterion are combined with quantization to form our improved KRLS algorithm. In the process of online prediction, it can bring computational efficiency up and adjust weights adaptively in time-varying environments. Moreover, Lorenz chaotic time series, El Nino-Southern Oscillation indexes chaotic time series, yearly sunspots and runoff of the Yellow River chaotic time series online prediction are presented to prove the effectiveness of our proposed algorithm.

Laplacian Echo State Network for Multivariate Time Series Prediction

Echo state network is a novel kind of recurrent neural networks, with a trainable linear readout layer and a large fixed recurrent connected hidden layer, which can be used to map the rich dynamics of complex real-world data sets. It has been extensively studied in time series prediction. However, there may be an ill-posed problem caused by the number of real-world training samples less than the size of the hidden layer. In this brief, a Laplacian echo state network (LAESN), is proposed to overcome the ill-posed problem and obtain low-dimensional output weights. First, an echo state network is used to map the multivariate time series into a large reservoir. Then, assuming that an unknown underlying manifold is inside the reservoir, we employ the Laplacian eigenmaps to estimate the manifold by constructing an adjacency graph associated with the reservoir states. Finally, the output weights are calculated by the low-dimensional manifold. In addition, some criteria of transient stability, local controllability, and local observability are given. Experimental results based on two real-world data sets substantiate the effectiveness and characteristics of the proposed LAESN model.

TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds

The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

Adaptive Elastic Echo State Network for Multivariate Time Series Prediction

Echo state network (ESN) is a new kind of recurrent neural network with a randomly generated reservoir structure and an adaptable linear readout layer. It has been widely employed in the field of time series prediction. However, when high-dimensional reservoirs are utilized to predict multivariate time series, there may be a collinearity problem. In this paper, to overcome the collinearity problem and obtain a sparse solution, we propose a new model-adaptive elastic ESN, in which adaptive elastic net algorithm is used to calculate the unknown weights. It combines the strengths of the quadratic regularization and the adaptively weighted lasso shrinkage. Hence, the proposed model can deal with the collinearity problem and enjoy the oracle property with an unbiased estimation. We exhibit the merits of our model on two benchmark multivariate chaotic datasets and two real-world applications. Experimental results substantiate the effectiveness and characteristics of the proposed model.

Stochastic Set-Based Particle Swarm Optimization Based on Local Exploration for Solving the Carpool Service Problem

The growing ubiquity of vehicles has led to increased concerns about environmental issues. These concerns can be mitigated by implementing an effective carpool service. In an intelligent carpool system, an automated service process assists carpool participants in determining routes and matches. It is a discrete optimization problem that involves a system-wide condition as well as participants' expectations. In this paper, we solve the carpool service problem (CSP) to provide satisfactory ride matches. To this end, we developed a particle swarm carpool algorithm based on stochastic set-based particle swarm optimization (PSO). Our method introduces stochastic coding to augment traditional particles, and uses three terminologies to represent a particle: 1) particle position; 2) particle view; and 3) particle velocity. In this way, the set-based PSO (S-PSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOs-S-PSO and binary PSO (BPSO)-and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a real-world metropolis. We observed that the S-PSO outperformed the BPSO and the GA thoroughly. Moreover, our method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP.

Multi-Input Single Output SSSC based damping controller design by a hybrid Improved Differential Evolution-Pattern Search approach

In this paper, a hybrid Improved Differential Evolution and Pattern Search (hIDEPS) approach is proposed for the design of a PI-Type Multi-Input Single Output (MISO) Static Synchronous Series Compensator (SSSC) based damping controller. The improvement in Differential Evolution (DE) algorithm is introduced by a simple but effective scheme of changing two of its most important control parameters i.e. step size and crossover probability with an objective of achieving improved performance. Pattern Search (PS) is subsequently employed to fine tune the best solution provided by modified DE algorithm. The superiority of a proposed hIDEPS technique over DE and improved DE has also been demonstrated. At the outset, this concept is applied to a SSSC connected in a Single Machine Infinite Bus (SMIB) power system and then extended to a multi-machine power system. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers. It is observed that the proposed approach yield superior damping performance compared to some approaches available in the literature.

Day-ahead crude oil price forecasting using a novel morphological component analysis based model

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

Multistep-ahead air passengers traffic prediction with hybrid ARIMA-SVMs models

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines' monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy.