Performance evaluation of series and parallel strategies for financial time series forecasting

Background:Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers.Given its direct impact on related decisions,various attempts have been made to achieve more accurate and reliable forecasting results,of which the combining of individual models remains a widely applied approach.In general,individual models are combined under two main strategies:series and parallel.While it has been proven that these strategies can improve overall forecasting accuracy,the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model.Methods:Therefore,this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one.Results:Accordingly,the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price.To do so,autoregressive integrated moving average(ARIMA)and multilayer perceptrons(MLPs)are used to construct two series hybrid models,ARIMA-MLP and MLP-ARIMA,and three parallel hybrid models,simple average,linear regression,and genetic algorithm models.Conclusion:The empirical forecasting results for two benchmark datasets,that is,the closing of the Shenzhen Integrated Index(SZII)and that of Standard and Poor’s 500(S&P 500),indicate that although all hybrid models perform better than at least one of their individual components,the series combination strategy produces more accurate hybrid models for financial time series forecasting.

Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies

This main contribution of this work is to propose a new approach based on a structure of MLPs （multi-layer perceptrons） for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that arc generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems.

Seasonal Based Electricity Demand Forecasting Using Time Series Analysis

Consumption of the electric power highly depends on the Season under consideration. The various means of power generation methods using renewable resources such as sunlight, wind, rain, tides, and waves are season dependent. This paves the way for analyzing the demand for electric power based on various Seasons. Many traditional methods are utilized previously for the seasonal based electricity demand forecasting. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. In this paper, a WEKA time series forecasting is being done for the electric power demand for the three seasons such as summer, winter and rainy seasons. The monthly electric consumption data of domestic category is collected from Tamil Nadu Electricity Board (TNEB). Data collected has been pruned based on the three seasons. The WEKA learning algorithms such as Multilayer Perceptron, Support Vector Machine, Linear Regression, and Gaussian Process are used for implementation. The Mean Absolute Error (MAE) and Direction Accuracy (DA) are calculated for the WEKA learning algorithms and they are compared to find the best learning algorithm. The Support Vector Machine algorithm exhibits low Mean Absolute Error and high Direction Accuracy than other WEKA learning algorithms. Hence, the Support Vector Machine learning algorithm is proven to be the WEKA learning algorithm for seasonal based electricity demand forecasting. The need of the hour is to predict and act in the deficit power. This paper is a prelude for such activity and an eye opener in this field.

快速和高精度的前馈网络学习算法

本文针对三层前馈网络提出一种全新的学习算法。该法克服了传统BP算法因用梯度下降和误差逆向传播而拖慢收敛速度及易陷于局部极小的缺点。所提出的算法是代数型的，计算复杂度为多项式阶。文中给出的一个非线性时间序列训练算例表明：新算法较BP算法在计算精度和速度方面均有大幅度提高，在网络规模变大时此算法的优点尤为明显。

水文时间序列趋势预测挖掘系统研究

讨论了时间序列趋势预测研究的现状和典型方法，并在时间序列预测中引入神经网络方法。介绍了水文时间序列趋势预测挖掘系统的设计与实现，详细分析了系统采用的时间序列预测的神经网络方法。

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems.The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays.Relied on learning an amount of information from given data,the long short-term memory(LSTM)method and multi-layer neural networks(MNN)method are applied to predict solutions.Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio,single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale.The predicted results by deep learning algorithms are well-agreed with experimental data.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

基于MLPs-dynFWA模型的高速铁路短时客流预测方法研究

针对传统深度学习模型在预测高速铁路短时客流时难以确定时间阈值、网络参数选取有难度等问题,提出一种基于多层感知器时间序列网络与动态搜索烟花算法的高速铁路预测模型。首先以高速铁路短时客流预测作为研究对象,将MLPs网络中每个节点作为一个感知器,模拟生物神经网络中神经元基础功能,对时间变化特征进行建模;再将dynFWA算法应用到神经网络参数多样性选择中,利用爆炸算子搜索机制对网络超参数组合进行优化,以高速铁路历史真实客流系数为基础,部分数据作为数据源,部分数据作为验证组,通过MLPs-dyn FWA模型进行预测并将结果与其他预测模型进行比较,得到不同数据组在不同模型优化策略下的性能指标。通过实验结果得知,MLPs-dynFWA模型对于高速铁路短时客流预测性能最优。

基于MLP和SARIMA的青岛市AQI预报模型

为掌握青岛市空气质量变化特征,为空气质量管控提供参考,以2014—2021年青岛市空气质量指数月统计历史数据为基础,通过深度学习算法中的多层神经网络建立了AQI与PM_(2.5)等6个主要污染物的预报模型,对青岛市空气质量的影响因素进行研究,并基于SARIMA模型预测了各污染物的浓度值,结合污染物浓度预测值和预报模型对AQI值进行了预测。根据预测结果,给出了改善青岛市空气质量的建议。

基于深度学习的时间序列趋势预测对比研究

时间序列趋势预测广泛应用在各种场景中,本文讨论了在仅使用历史数据的情况下时间卷积网络(Temporal Convolutional Network,TCN)、卷积神经网络(Convolutional Neural Networks,CNN)、多层感知器(Multi-Layer Perceptron,MLP)在时间序列趋势预测中的性能。为避免数据集对不同神经网络结构的影响,本文采用了单一模型与训练两个独立的神经网络模型来预测开普敦气温、标普500每日收盘价以及家庭用电量数据集趋势的斜率和持续时间,并进行对比研究。实验结果表明,在单一模型预测中,CNN性能最佳。同时,所有的网络模型结构,独立模型分开预测的方式都优于传统单一网络的趋势预测性能。