This paper aims to find a forecasting model based on the comparative study of wavelet- ARIMA and EMD-ARIMA models residual-based selection technique for temperature time series. Time series analysis is essential in studying data investigating variation predicting future trend, which we can control changes make good decisions. And most important understand trend with time. applied hybridized wav...

Most of today’s time series data contain anomalies and multiple seasonalities, accurate anomaly detection in these is critical to almost any type business. However, most mainstream forecasting models used for can only incorporate one or no seasonal component into their forecasts cannot capture every known pattern data. In this paper, we propose a new multi-seasonal model that extends the popula...

Abstract In the present study, seasonal autoregressive integrated moving average (SARIMA) time series models, nonlinear BL model, multi-layer perceptron artificial neural network and SARIMA-bilinear hybrid models were employed to predict quality parameters of total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC) in Maroon basin Khuzestan Province. For fitting...

Many variations such as the annual cycle of sea surface temperatures can be considered to be smooth functions and are appropriately described using methods from functional data analysis. This study de-nes a class of Functional Auto-Regressive (FAR) models which can be used as robust predictors for making forecasts of entire smooth functions in the future. The methods are illustrated and compare...

As a supplement to or extension of methods used to determine trends in foodborne illness over time, we propose the use of Internet search metrics. We compared Internet query data for foodborne illness syndrome-related search terms from the most popular 5 Korean search engines using Health Insurance Review and Assessment Service inpatient stay data for 26 International Classification of Diseases...

BACKGROUND Determining the temporal variations and seasonal pattern of diseases and forecasting their incidence can help in promoting disease control and management programs. This study was performed to determine the seasonal variation of tuberculosis and forecast its incidence until the year 2015 in one of the northern provinces of Iran. MATERIALS AND METHODS A longitudinal time series study...