Forecasting of daily air quality index in Delhi

As the impact of air pollutants on human health through ambient air address much attention in recent years, the air quality forecasting in terms of air pollution parameters becomes an important topic in environmental science. The Air Quality Index (AQI) can be estimated through a formula, based on comprehensive assessment of concentration of air pollutants, which can be used by government agencies to characterize the status of air quality at a given location. The present study aims to develop forecasting model for predicting daily AQI, which can be used as a basis of decision making processes. Firstly, the AQI has been estimated through a method used by US Environmental Protection Agency (USEPA) for different criteria pollutants as Respirable Suspended Particulate Matter (RSPM), Sulfur dioxide (SO₂), Nitrogen dioxide (NO₂) and Suspended Particulate Matter (SPM). However, the sub-index and breakpoint concentrations in the formula are made according to Indian National Ambient Air Quality Standard. Secondly, the daily AQI for each season is forecasted through three statistical models namely time series auto regressive integrated moving average (ARIMA) (model 1), principal component regression (PCR) (model 2) and combination of both (model 3) in Delhi. The performance of all three models are evaluated with the help of observed concentrations of pollutants, which reflects that model 3 agrees well with observed values, as compared to the values of model 1 and model 2. The same is supported by the statistical parameters also. The significance of meteorological parameters of model 3 has been assessed through principal component analysis (PCA), which indicates that daily rainfall, station level pressure, daily mean temperature, wind direction index are maximum explained in summer, monsoon, post-monsoon and winter respectively. Further, the variation of AQI during the weekends (holidays) and weekdays are found negligible. Therefore all the days of week are accounted same in the models.     

A PROTOTYPICAL SEASONAL ADJUSTMENT MODEL

Abstract. The paper analyses unobserved-components modelling and estimation for the simplest ARIMA process that accepts a full decomposition into trend, seasonal and irregular components. This prototypical model exemplifies many features of and issues arising in model-based seasonal adjustment that are less transparent in more complex seasonal time series models. In particular the analysis illuminates the major issues surrounding the specification of the component models and the identification of a unique structure for them. In so doing, the relationship between reduced- and structural-form approaches to unobserved components estimation is illustrated within an ARIMA-modelling framework. Finally, the properties of the minimum mean-squared-error estimators of the unobserved components are examined and the two main types of estimation error, revisions in the preliminary estimator and error in the final estimator, are analysed.     

DETERMINING THE DEGREE OF DIFFERENCING FOR TIME SERIES VIA THE LOG SPECTRUM

Abstract. While many time series require differencing before a model may be fitted it has been shown that 'overdifferencing' may result in a fitted model with poor long term forecasting properties. This may present real problems when the degree of differencing which is appropriate is fractional. We show that the log spectrum is a natural quantity to consider when attempting to determine the degree of differencing required and outline the distribution theory required. The ideas are shown to extend to the seasonal case and can be used to assess whether seasonal differencing is appropriate.     

Time series study of the impact of serious fires on fire occurrence statistics in cities of Jiangsu

In this paper, the time series method is used to analyze the effect of fire disasters and serious fires on monthly fire occurrence statistics in 10 cities from January 1997 to December 2001 in Jiangsu province. After removal of outliers in the irregular component of the original data series and seasonal adjustments, the intervention model is applied to quantify the effect of the fire disasters and serious fires on the total monthly fire statistics in the cities of Jiangsu province. The results show that the impact on monthly fires lasts for 3 months for eight cities and 2 months for two cities. Other phenomena are also detected in these 10 cities. Finally, some explanations for these phenomena are proposed.     

基于非线性时间序列的预测模型检验与优化的研究

 模型的适用性检验和参数优化是系统建模的最关键环节,对于预测模型的适用性检验,常采用残差方差图、最小信息准则和AIC准则等方法,存在计算量大、准确性低、模型不唯一等缺点.本文给出采用自相关系数和偏自相关系数的拖尾先对ARIMA模型检验,再对其进行F适用性检验,克服了由于观测样本的长度是有限的,偏相关的估计存在误差,拖尾时不能为ARMA定阶的缺陷,并采用具有超线性收敛性等诸多优点的变尺度法对模型参数进行了优化,得到了较为精确的、单一AIRMA 模型,该方法可应用于网络流量模型的适用性检验和模型优化,为网络流量的预测、异常检测和服务器负载预测的应用奠定了坚实的基础.     

移动网无线信号变化预测研究

鉴于移动通信网规模日益扩大,网络运营状态不易即时掌控的现状,提出了一种用于观测移动网无线信号实时变化的新型监控系统。在该系统长期运行而获得的采样数据基础上,对某处小区的无线信号变化特性进行了研究。运用SPSS统计工具和时间序列中的Box—Jenkins的建模方法,分别建立了AR、MA、ARMA、ARIMA模型对实测数据进行了分析和预测,然后对不同模型的预测结果进行了误差分析,结果表明ARIMA（1,1,1）模型准确性最高,误差最小,能对短期内的无线信号变化趋势进行预测。     

含置信区间的ARIMA与GARCH混合式电价预测方法研究

通过对已有的各种电价预测方法的深入研究,提出一种带置信区间的混合式电价预测方法.比较了自相关函数和偏自相关函数判断平稳性及残差方差图定阶法,认为游程检验判断电价序列平稳性方法和AIC准则模型定阶法可避免预测过程中的主观因素,提高预测精度.通过引入电价误差序列异方差判断,结合ARIMA和GARCH,提出含置信区间的电价预测方法,极大地克服了单点预测缺点,增加算法的灵活性,使得市场竞价者可以根据自己对电价预测精度的期望选择电价波动的范围.采用PJM市场数据,验证了算法的有效性.     

基于ARIMA模型的渔业经济预测及其优化

渔业作为国民经济的重要基础之一，对其进行预测十分必要。本文采用时间序列ARIMA模型对渔业总产值进行预测，根据模型预测结果进行误差分析。考虑通货膨胀对预测模型的影响，利用居民消费价格指数（CPI）对模型进行进一步优化。进而以江苏省渔业总产值为例，将1995-2014年的数据作为训练样本，建立模型并结合CPI指数对其优化，以2015-2018年数据作为测试样本，验证了优化模型具有较好的预测效果。     

基于ARIMA和BP神经网络的生态系统服务价值预测仿真

针对现有生态系统服务价值预测体系误差大、研究不足等问题,本文构建了面向未来生态服务价值的预测机制。该机制采用主成分分析法和Person相关系数法,建立了以GDP、人口、土地利用率、土地管理政策和城市人口密度为驱动力的生态系统服务价值关联矩阵,提出了基于ARIMA和BP神经网络的生态系统服务价值的时间序列预测机制。为验证预测机制有效性,本文选取中国主要土地利用类型代表省份的真实土地数据集进行分析,研究结果表明本文建立的预测模型平均绝对误差仅为0.023,且从预测结果来看,未来草地生态会向较好趋势发展,林地生态发展不容乐观。     

Evaluating the value of information sharing in a supply chain using an ARIMA model

This paper considers a two-echelon supply chain, which contains one supplier and one retailer. It studies the quantification of the bullwhip effect and the value of information-sharing between the supplier and the retailer under an autoregressive integrated moving average (ARIMA) demand of (0, 1, q). The results show that with an increasing value of q, bullwhip effects will be more obvious, no matter whether there is information sharing or not. When there exists information sharing, the value of the bullwhip effect is greater than it is without information sharing. With an increasing value of q, the gap between the values of the bullwhip effect in the two cases will be larger.