Flooding is the most common natural disaster and continues to increase in frequency intensity due climate changes [7]. Currently, there a lack of efficient tools predict flooding. This research aimed create Time Series Machine Learning (ML) program using Auto Regressive Moving Average (ARIMA) models forecast streamflow, one prominent factors flood prediction. A streamflow dataset from Ganges Ri...

in this study, the situation of iran, u.s and turkey's pistachio export is investigated. to this purpose, revealed comparative advantage (rca) index is calculated based on agricultural and total economy export, separately, then forecasted by using auto- regressive integrated moving average (arima) approached, for 2008-2013. the results show that considering both commodity baskets, turkey and ir...

Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop strategies to improve forecasting accuracy. One of the most well established and widely use...

Received Jul 22, 2012 Revised Oct 23, 2012 Accepted Nov 14, 2012 Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades but not very often used to forec...

This article presents the results of reviewing predictive capacity Google Trends for national elections in Chile. The electoral between Michelle Bachelet and Sebastián Piñera 2006, Eduardo Frei 2010, Evelyn Matthei 2013, Alejandro Guillier 2017, Gabriel Boric José Antonio Kast 2021 were reviewed. time series analyzed organized on basis relative searches candidacies, assisted by R software, main...

It is an important issue to study the prediction precision of Particulate Matter 2.5, PM2.5 (28 μg/m3), concentration change. The concentration of PM2.5 is influenced by many factors, and its change is characterized by non-linearity and randomness. This paper establishes a prediction model of PM2.5 concentration change to fit the nonlinear and random trend by combining Auto-Regressive Integrate...

This paper presents the adaptation of CORBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra virgin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire population is responsible for the final solution. In orde...

in this study, the situation of iran, u.s and turkey's pistachio export is investigated. to this purpose, revealed comparative advantage (rca) index is calculated based on agricultural and total economy export, separately, then forecasted by using auto- regressive integrated moving average (arima) approached, for 2008-2013. the results show that considering both commodity baskets, turkey a...

Air pollution is a worldwide issue that affects the lives of many people in urban areas. It considered air may lead to heart and lung diseases. A careful timely forecast quality could help reduce exposure risk for affected people. In this paper, we use data-driven approach predict based on historical data. We compare three popular methods time series prediction: Exponential Smoothing (ES), Auto...