A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

强化数据预处理的BLSTNet-CBAM短期电力负荷预测

针对负荷数据复杂性、非平稳性以及负荷预测误差较大等问题,提出一种综合特征构建和模型优化的短期电力负荷预测新方法.首先采用最大信息系数(MIC)分析特征变量的相关性,选取与电力负荷序列相关的特征变量,同时,考虑变分模态分解(VMD)方法容易受主观因素的影响,采用霜冰优化算法(RIME)优化VMD,完成原始电力负荷序列的分解.然后改进长短期时间序列网络(LSTNet)作为预测模型,将其递归层LSTM更新为BiLSTM,并引入卷积块注意力机制(CBAM)进行预测.通过对比实验和消融实验的结果表明:经RIME-VMD优化后,LSTM、GRU、LSTNet模型预测的均方根误差(RMSE)均降低20%以上,显著提高模型预测精度,且能够适应于不同预测模型.所提出的BLSTNet-CBAM模型与LSTM、GRU、LSTNet相比,RMSE分别降低了35.54%、6.78%、1.46%,提高了短期电力负荷预测的准确性.

基于近邻传播聚类与LSTNet的分布式光伏电站群短期功率预测

为了应对分布式光伏渗透率不断提高带给电网运行的挑战,提出了一种基于近邻传播聚类与长短期时间序列网络(LSTNet)的区域分布式光伏电站群短期功率预测模型。首先,利用近邻传播算法划分区域内不同季节的分布式光伏电站群,并通过皮尔逊相关系数确定光伏出力的强相关气象因子,结合双线性插值法加密对应光伏电站群的气象数据。然后,通过LSTNet挖掘光伏功率和气象因子序列的长期和短期时空依赖,并叠加自回归的线性分量,实现了群内多个光伏电站的同时预测。最后,利用美国国家能源部可再生能源实验室的实测数据集验证了所提方法的有效性。实验比较表明,所提预测模型具有较高的预测精度和鲁棒性。

基于LSTNet模型的配电台区短期电力负荷预测研究

短期电力负荷预测作为电力系统运行规划的重要依据,对电力系统的安全经济运行有重要意义。提出一种长期和短期时间序列网络(LSTNet)模型对配电台区的短期负荷变化进行预测。该模型用卷积神经网络(CNN)提取负荷数据间的局部依赖关系,用长短时记忆(LSTM)神经网络提取负荷数据长期变化趋势,再融合传统自回归模型解决神经网络对负荷数据极端值的不敏感问题,最后将某一配电台区的电力负荷数据用于网络的训练和预测过程中。通过仿真实验案例发现,相较于以往LSTM、双向长短时记忆神经网络(Bi-LSTM)和CNN-LSTM的预测模型,LSTNet模型在短期负荷预测方面更具优势、预测精度更高。

Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model

By predicting influent quantity,a wastewater treatment plant(WWTP)can be well controlled.The non-linear dynamic characteristic of WWTP influent quantity time series was analyzed,with the assumption that the series was predictable.Based on this,a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction.Reasonable forecasting results were achieved using this method.

电表数据隐私保护下的联邦学习行业电力负荷预测框架

电力市场化改革背景下,供电公司的用电采集系统不对主动配电网运营商开放,同时,未来终端用户更倾向于将用户信息保存在本地以保护自己的隐私,主动配电网运营商需要在无读表权的条件下开展负荷预测等电力业务。为此,选择天气和时间因素作为负荷的关联因素,提出一种面向行业用户读表数据保护的联邦学习负荷预测框架。在此基础上,构建了行业用户数据集,基于长短期时间序列网络(LSTNet)建立负荷预测模型,同时利用FedML框架建立基于联邦学习的分行业负荷预测框架。算例分析表明,所述方法能使同行业的用户在不共享负荷数据的前提下进行联邦训练,在保护用户用电隐私的前提下支撑主动配电网运营商相关业务开展,具有较优的预测性能、较少的模型数量和较短的耗时。

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.

Short-term power load forecasting using integrated methods based on long short-term memory

The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward higher requirements for the accuracy and stability of load forecasting.In this paper,an integrated network architecture which consists of the self-organized mapping,chaotic time series,intelligent optimization algorithm and long short-term memory(LSTM)is proposed to extend the load forecasting length,decrease artificial debugging,and improve the prediction precision for the short-term power load forecasting.Compared with LSTM prediction,the algorithm in this paper improves the prediction accuracy by 61.87%in terms of root mean square error(RMSE),and reduces the prediction error by 50%in the 40-fold forecast window under some circumstances.

Performance evaluation of new echo state networks based on complex network

Recently, echo state networks （ESN） have aroused a lot of interest in their nonlinear dynamic system modeling capabilities. In a classical ESN, its dynamic reservoir （DR） has a sparse and random topology, but the performance of ESN with its DR taking another kind of topology is still unknown. So based on complex network theory, three new ESNs are proposed and investigated in this paper. The small-world topology, scale-free topology and the mixed topology of small-world effect and scale-free feature are considered in these new ESNs. We studied the relationship between DR architecture and prediction capability. In our simulation experiments, we used two widely used time series to test the prediction performance among the new ESNs and classical ESN, and used the independent identically distributed （i.i.d） time series to analyze the short-term memory （STM） capability. We answer the following questions： What are the differences of these ESNs in the prediction performance？ Can the spectral radius of the internal weights matrix be wider？ What is the short-term memory capability？ The experimental results show that the proposed new ESNs have better prediction performance, wider spectral radius and almost the same STM capacity as classical ESN＇s.