Abstract We adapt the classical definition of locally stationary processes in discrete time (see e.g. Dahlhaus, ‘Locally processes’, Time Series Analysis: Methods and Applications (2012)) to continuous-time setting obtain equivalent representations frequency domains. From this, a unique time-varying spectral density is derived using Wigner–Ville spectrum. As an example, we investigate Lévy-driv...

One of the most important hydrological time series task is to determine if there is any trend in the data and how to achieve stationarity when there is nonstationarity behavior in data. Detecting trend and stationarity in hydrological time series may help us to understand the possible links between hydrological processes and global climate changes. In this study yearly, monthly and daily stream...

in the management of river basins, prediction of river water quality is essential to maintain water quality within standard limits. this study performed a time-series analysis of the prediction of chlorine concentration and electrical conductivity time series data for the period of 1991-2005 from sefidrood river in northern iran. the seasonal prediction of chlorine and electrical conductivity t...

This study analysed the dynamics of international soybean market using econometric techniques and economic models to impacts US–China trade war. It considered analysis “spatial” (horizontal) price transmission during an approximately ten-year period from September 2009 May 2019 monthly time-series data. The research focused on leaders in market, namely, China, USA, EU, Brazil Argentina. Several...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...