This study compares two new seasonal adjustment methods designed to handle outliers and structural changes: X-IZARIMA and GAUSUM-STM. X12-ARIMA is a successor to the X-ll-ARIMA seasonal adjustment method, and is being developed at the U.S. Bureau of the Census (Findley et al. (1988)). GAUSUM-STM is a non-Gaussian method using time series structural models, and was developed for this study based...

This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA mode...

For example, it has long been recognized that single exponential forecasting (SES) is equivalent to an ARIMA(0,1,1) model (e.g., Harvey, 1989) The additional flexibility of ARIMA models may be thought to lead to more accurate empirical forecasts. However, Table 13 of Makridakis and Hibon shows that there is virtually no improvement in forecasting accuracy using ARIMA models (labeled B-J automat...

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be le...

This paper presents a comprehensive study of ANFIS+ARIMA+IT2FLS models for forecasting the weather of Raipur, Chhattisgarh, India. For developing the models, ten year data (2000-2009) comprising daily average temperature (dry-wet), air pressure, and wind-speed etc. have been used. Adaptive Network Based Fuzzy Inference System (ANFIS) and Auto Regressive Moving Average (ARIMA) models based on In...

Forecasting accuracy is one of the most favorable critical issues in Autoregressive Integrated Moving Average (ARIMA) models. The study compares the application of two forecasting methods on the amount of Taiwan export, the Fuzzy time series method and ARIMA method. Model discussed for the ARIMA method and Fuzzy time series method include the Sturges rules. When the sample period is extend in o...

hydrologists have always tried to classify atmospheric and hydrologic events in order to simplify the hydrologic convolutions and the observations or to save the time and the budget. most of these methods are used for the regionalization of hydrologic phenomena like rainfall,streamflow and other components of water cycle.multivariate techniques have been underlined as suitableand powerful tools...

Massive actuator arrays found their applications in robotics, pharmaceutics, aerospace, etc. Compared with electrically and/or pneumatically powered actuator array, hydraulic actuator has the comprehensive advantages of higher force density (i.e. force / actuation area), better controllability, and simpler remote control accessibility. This paper presents approaches to construct and control a m...

In this article, we forecast crude oil and natural gas spot prices at a daily frequency based on two classification techniques: artificial neural networks (ANN) and support vector machines (SVM). As a benchmark, we utilize an autoregressive integrated moving average (ARIMA) specification. We evaluate outof-sample forecast based on encompassing tests and mean-squared prediction error (MSPE). We ...