FIXED-DESIGN SEMIPARAMETRIC REGRESSION FOR LINEAR TIME SERIES

This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained under suitable conditions. Finally, the author shows that the usual weight functions based on nearest neighbor methods satisfy the designed assumptions imposed.

Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

Small-time scale network traffic prediction based on a local support vector machine regression model

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

Asymptotics of estimators for nonparametric multivariate regression models with long memory

In this paper,a nonparametric multivariate regression model with long memory covariates and long memory errors is considered.We approximate the nonparametric multivariate regression function by the weighted additive one-dimensional functions.The local linear smoothing and least squares method are proposed for the one-dimensional regression estimation and the weight parameters estimation,respectively.The asymptotic behaviors of the proposed estimators are investigated.

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.

Parameter Estimation of Time-Varying ARMA Model

The auto-regressive moving-average (ARMA) model with time-varying parameters is analyzed. The time-varying parameters are assumed to be a linear combination of a set of basis time-varying functions, and the feedback linear estimation algorithm is used to estimate the time-varying parameters of the ARMA model. This algorithm includes 2 linear least squares estimations and a linear filter. The influence of the order of basis time-(varying) functions on parameters estimation is analyzed. The method has the advantage of simple, saving computation time and storage space. Theoretical analysis and experimental results show the validity of this method.

Detecting DDoS Attacks against Web Server Using Time Series Analysis

Distributed Denial of Service （DDoS） attack is a major threat to the availability of Web service. The inherent presence of self-similarity in Web traffic motivates the applicability of time series analysis in the study of the burst feature of DDoS attack. This paper presents a method of detecting DDoS attacks against Web server by analyzing the abrupt change of time series data obtained from Web traffic. Time series data are specified in reference sliding window and test sliding window, and the abrupt change is modeled using Auto-Regressive （AR） process. By comparing two adjacent nonoverlapping windows of the time series, the attack traffic could be detected at a time point. Combined with alarm correlation and location correlation, not only the presence of DDoS attack, but also its occurring time and location can be deter mined. The experimental results in a test environment are illustrated to justify our method.

A K-S type test of linearity for a class of time series models

1 Theorems Consider the following nonlinear autoregressive modelxt=φxt-1+εth(xt-1θ) （1）with the assumptions:（A1）│φ│【1,θ=（θ0,θ1）∈ is an open set in R2,（A2） {εt} is a sequenee of independent identically distributed random variables suchthatEεt=0, Eεt2=1, E│εt│4+δ【∞,for some δ】0, （2）and εt is independent of xt-1, （A3） h（·） is an everywhere positive measurable function satisfying that as│x │→∞,h（x）→∞, h（x）/│x│→0, and for each C】0, sup h（x）【∞,

A Novel Hybrid FA-Based LSSVR Learning Paradigm for Hydropower Consumption Forecasting

Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.

Time-varying parameter auto-regressive models for autocovariance nonstationary time series

In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

Feature selection for energy system modeling: Identification of relevant time series information

Heuristic or clustering based time series aggregation methods are often used to reduce temporal complexity of energy system models by selecting representative days.However,these methods potentially neglect relevant information of time series(e.g.,distribution parameters).To identify relevant time series parameters,feature selection algorithms can be applied.The present research contributes by(a)developing a new feature selection approach based on clustering,nested modeling and regression(CNR)which is designed for applications requiring high selectivity and using different data sets,(b)comparing and evaluating CNR with feature selection methods available from the literature(e.g.,LASSO)and(c)identifying relevant information of the time series applied in energy system models,in particular those of demand,photovoltaic and wind.Results show that CNR achieves on average up to 101%lower mean absolute errors when methods are directly compared.Thus,CNR better identifies relevant information when the number of selected features is restricted.The disadvantage of CNR,however,is its high computational effort.A potential remedy to counter this is the combination with another method(e.g.,as pre-feature selection).In terms of relevant information,energy systems including photovoltaic are mainly characterized by the correlation between demand and photovoltaic time series as well as the range and the 35%quantile of demand.When energy systems include wind power,the minimum and mean of wind as well as the correlation between demand and wind time series are relevant characteristics.The implications of these findings are discussed.

DEPENDENCE ANALYSIS OF REGRESSION MODELS IN TIME SERIES

In this paper, the relative dependence of a linear regression model is studied. In particular, the dependence of autoregressive models in time series are investigated. It is shown that for the first-order non-stationary autoregressive model and the random walk with trend and drift model, the dependence between two states decreases with lag. Some numerical examples are presented as well.

A Hybrid Methodology for Short Term Temperature Forecasting

Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

A Water Level Forecast of Pattani River in the Southern of Thailand by Deep Learning

Nowadays, the deep learning methods are widely applied to analyze and predict the trend of various disaster events and offer the alternatives to make the appropriate decisions. These support the water resource management and the short-term planning. In this paper, the water levels of the Pattani River in the Southern of Thailand have been predicted every hour of 7 days forecast. Time Series Transformer and Linear Regression were applied in this work. The results of both were the water levels forecast that had the high accuracy. Moreover, the water levels forecasting dashboard was developed for using to monitor the water levels at the Pattani River as well.

Bayesian-combined wavelet regressive modeling for hydrologic time series forecasting

Wavelet regression(WR)models are used commonly for hydrologic time series forecasting,but they could not consider uncertainty evaluation.In this paper the AM-MCMC(adaptive Metropolis-Markov chain Monte Carlo)algorithm was employed to wavelet regressive modeling processes,and a model called AM-MCMC-WR was proposed for hydrologic time series forecasting.The AM-MCMC algorithm is used to estimate parameters’uncertainty in WR model,based on which probabilistic forecasting of hydrologic time series can be done.Results of two runoff data at the Huaihe River watershed indicate the identical performances of AM-MCMC-WR and WR models in gaining optimal forecasting result,but they perform better than linear regression models.Differing from the WR model,probabilistic forecasting results can be gained by the proposed model,and uncertainty can be described using proper credible interval.In summary,parameters in WR models generally follow normal probability distribution;series’correlation characters determine the optimal parameters values,and further determine the uncertain degrees and sensitivities of parameters;more uncertain parameters would lead to more uncertain forecasting results and hard predictability of hydrologic time series.

Rainfall and Obtaining Information Regarding Earthquake Development Processes from Groundwater Level

Many factors can cause changes of groundwater level,such as the development process of an earthquake,rainfall,solid earth tides etc.Among these we are interested in information regarding earthquake development processes.Eliminating the influence of various disturbance factors is an effective way to obtain seismic development process information contained in the groundwater level.This paper provides two different ways to remove the rainfall effect,and compares the two methods by means of correlation analysis.Furthermore,based on these a logistic regression model is established to describe the seismicity level.

间歇性时间序列数据多目标决策挖掘算法设计

时序数据规模化存储能大幅度提升经济效益,但传统挖掘算法无法从海量数据中提取有效信息。为解决上述难题,通过对数据进行优化处理,提出一种MAD-SVR时序数据回归预测算法。算法首先对大数据进行标准化处理,并通过极值点分析与剔除,提升数据的有效性;然后采用多目标MIC相关性分析方法,提高对标准时序数据的间歇性特征提取能力;接着利用AHP层次分析量化指标,获取最优簇N,并基于DIANA算法完成时序数据聚类优化过程;最后通过十折交叉验证的方式,构建SVR时序数据回归预测模型,完成预测结果输出。不同叠加模型的仿真对比结果表明,较其他模型相比,MADSVR模型的MAPE参数整体减少了53.89%,R^(2)参数增加了5.99%,且RMSE参数至少下降了12.31%,即其该模型的拟合度最高,预测能力最优,且预测真实占比有较大提高,但预测真实占比误差偏离度尚有优化空间。综上,MAD-SVR算法在海量时序数据挖掘中具有重要的仿真研究价值。

基于自回归模型的RBCC隔离段激波串位置识别与压力值预估

为了清楚客观地判断火箭基组合循环发动机(Rocket-based combined-cycle,RBCC)隔离段激波串位置,将Ma=6,4,3.5工况下直连试验中实测得到的RBCC隔离段测压点压力数据按照时间的先后顺序排列形成一时间序列,建立自回归(Auto-Regressive,AR)模型并计算赤池信息准则(Akaike information criterion,AIC)值,完成了不同工况下激波串前缘位置的识别。研究表明:当隔离段测压点没有受到激波串影响时,实时压力值仅存在微弱波动,模型AIC值变化较为平稳;当激波串运动至测压点处时,该点压力升高,振荡幅度明显增加,AIC值随之瞬时增大。取同一时间段内发动机沿程测压点中首个AIC值增加500以上,并在不改变工况的情况下始终保持较大值的测点位置为激波串前缘位置。与压比法相比,时间序列分析法能敏感监测到实时压力值的升高和振荡,激波串前缘位置识别更为准确。通过建立自回归模型还可以实现激波串内部压力值预估,记录连续160 ms内Ma=6,4,3.5工况下测压点压力数据,采样频率1 kHz,使用前80 ms数据建立自回归模型,完成后80 ms压力值预估及准确性检验,得到三个工况下预估平均误差分别为3.21%,7.68%,6.49%。

日间手术对平均住院日影响的中断时间序列分析

目的 探讨日间手术的实施对医院平均住院日的影响。方法 从湖北省某三甲医院日间手术管理系统中调取全院、泌尿外科、眼科及耳鼻喉头颈外科2013年1月至2019年12月的平均住院日数据,采用中断时间序列模型分析日间手术实施前后各科室平均住院日的变化趋势。结果 日间手术的实施使全院、泌尿外科、眼科及耳鼻喉头颈外科平均住院日分别下降1.665 d(P<0.05)、3.501 d(P<0.05)、2.840 d(P<0.05)和5.767 d(P<0.05),其中,日间手术前平均每月分别下降0.026 d(P<0.05)、0.001 d(P=0.856)、0.004 d(P=0.425)和-0.005 d(P=0.383),日间手术后下降幅度分别增加了0.030 d(P<0.05)、0.073 d(P<0.05)、0.058 d(P<0.05)和0.104 d(P<0.05)。结论日间手术的实施可有效缩短平均住院日,提高医疗资源利用率。

昼夜温差对乌鲁木齐市慢性肾脏病日住院人次影响的时间序列分析

目的探讨昼夜温差(diurnal temperature range,DTR)影响慢性肾脏病(chronic kidney diseases,CKD)日住院人次的影响。方法收集2019年1月1日至2020年12月31日乌鲁木齐市4所三甲医院、4所二甲医院、1所一甲医院CKD日住院人次数据,同期气象及污染物数据来自于乌鲁木齐市主城区的6个国控监测点,采用分布滞后非线性模型,控制星期几效应、假期效应、长期时间趋势及其它因素,分析DTR与CKD日住院人次的关系。结果CKD日住院人次与DTR(滞后0~21 d)的暴露-反应曲线呈“N”形,CKD患者住院风险随DTR的升高呈先上升后下降趋势。低度和高度DTR对CKD患者住院的影响存在一定的滞后效应,中度DTR对住院影响较小;DTR=5℃时,单日效应出现在第3天[RR=1.081,95%CI(1.020,1.145),P<0.05],最大效应出现在第21天[RR=1.090,95%CI(1.014,1.173),P<0.05];高度DTR=14℃(P_(95))时,单日效应出现在第4天[RR=1.086,95%CI(1.007,1.172),P<0.05],最大效应出现在第5天[RR=1.089,95%CI(1.009,1.176),P<0.05],累积滞后均暂未发现有统计学差异。男性和年龄<65岁的CKD患者更易受到DTR的影响,寒冷季节和四季更替时DTR变化对CKD患者住院的影响更大。结论男性与<65岁CKD患者更易受到DTR的影响,在寒冷季节和四季交替DTR变化时更应重点保护易感人群免受DTR的影响。