Climate and rainfall are highly non-linear and complicated phenomena, which require sophisticated computer modelling and simulation for accurate prediction. An artificial intelligence technology allows knowledge processing and can be used .as forecasting tool. For example, the application of Artificial Neural Networks (ANN), to predict the behaviors of nonlinear systems has become an attractive...

As renewable energy increasingly integrates into the electric power system, electric load forecasting and renewable energy power generation forecasting become more important. In this project, ARIMA and NARX are applied to build load forecasting model focusing on improving statistical and computational efficiency without losing accuracy. ARIMA turns out to be better for short term forecasting wh...

We show that many time series data are governed by Geometric Brownian Motion (GBM) law. This motivates us to propose a procedure of time series model building for autocorrelated process control that might consist of two steps. First, we test whether the process data are governed by GBM law. If it is affirmative, the appropriate model is directly given by the properties of that law. Otherwise, w...

Tourism, one of the biggest industries in many countries, has been considered a complexly integrated and self-contained economic activity.As key determinants of thetourism demand are not fully identified to some extent different forecasting models vary in thelevel of accuracy. By comparing the performance of diverse forecasting models,including the linear regression, autoregressive integrated m...

Baltic Exchange Dry Index (BDI) is an independent response of maritime market information for the trading and settlement of physical and derivative contracts. In this paper, we propose fuzzy set theory and grey system for modeling the prediction of BDI, and employ the ARIMA model for the calibration of the data structure to depict the trend. The empirical results indicate that for both short-te...

Consider the fractional ARIMA time series with innovations that have innnite variance. This is a nite parameter model which exhibits both long-range dependence (long memory) and high variability. We prove the consistency of an estimator of the unknown parameters which is based on the periodogram and derive its asymptotic distribution. This shows that the results of Mikosch, Gadrich, Kl uppelber...

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...

background road traffic accidents and their related deaths have become a major concern, particularly in developing countries. iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. in this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying arima models on data...

This paper discusses the prediction of inflation rate in Indonesia. The data used this research is assumed to have both linear and non-linear components. ARIMA model selected accommodate component, while ANFIS method accounts for component data. Thus, known as hybrid ARIMA-ANFIS model. clustering performed using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 6 clusters. opti...