Hydrological drought refers to a persistently low discharge and volume of water in streams and reservoirs, lasting months or years. Hydrological drought is a natural phenomenon, but it may be exacerbated by human activities. Hydrological droughts are usually related to meteorological droughts, and their recurrence interval varies accordingly. This study pursues to identify a stochastic model (o...

Nonstationary ARIMA processes and nearly nonstationary ARMA processes, such as autoregressive processes having a root of the AR polynomial close to the unit circle, have sample autocovariance and spectral properties that are, in practice, almost indistinguishable from those of a stationary longmemory process, such as a Fractionally Integrated ARMA (ARFIMA) process. Because of this, model misspe...

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,...

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models the Box Jenkins approach and the objective penalty function methods. The emphasis is on forec...

In many intervention analysis applications, time series data may be expensive or otherwise difficult to collect. In this case the power function is helpful, because it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming that an underlying autoregressive integrated moving average (ARIMA) or fractional ARIMA model is...

Two widely-used seasonal adjustment programs are the U.S. Census Bureau's X-12-ARIMA and the SEATS program for ARIMA-model-based signal extraction written by Agustin Maravall. In previous studies with SEATS and X-12-ARIMA, we found some series where the adjustment from SEATS had smaller revisions than the adjustment from X-12-ARIMA (Hood, Ashley, and Findley, 2000). Based on this previous work,...

The well-known Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) methodology for fitting time-series data has some major limitations. To this end, Exponential Autoregressive (EXPAR) family of models may be employed. An important characteristic feature of EXPAR is that it is capable of modelling those data sets that depict cyclical variations. Further, it can also be used when data s...

BACKGROUND The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. METHODS In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 t...

in many applications the quality of a process or product is best characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. profile monitoring is used to understand and to check the stability of this relationship over time. therefore checking the capability of a profile seems essential. process capability indices have become very...

In recent years, urban buildings have become taller, occupying more and areas, frequent fires, increasingly difficult fire rescue tasks. Predicting risks in advance will help work. Therefore, this paper proposes a risk prediction based on the ARIMA model. By analyzing data of certain place from 2016 to 2020 January 1, 2016, December 31, 2019, an model for predicting number polices was establish...