This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the er...

The purpose of the paper is to extend recent results of Muellbauer (1986) for the life cycle consumption model under rational habit formation. It is shown that a general pattern of habits will lead to an arbitrary autoregressive integrated moving average (ARIMA) process for consumption. As a special case it is shown how a model in annual differences can be obtained by imposing a specific struct...

In this paper, I describe the theoretical background that led to the creation of software for interactive dance performance that interprets rhythm in dance movement as musical rhythm. This software was implemented as a library of external objects for Max/MSP[12] that processes data from an object or library that performs frame-differencing analysis of a video stream in real time in this program...

Strong coupling between values at different times that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The autoregressive fractional integral moving average (ARFIMA) model, a fractional order signal processing technique, is the generalization of the conventional integer order models—autoregressive integral ...

This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out ...

The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particu...

We study the autocorrelation structure and the spectral density function of aggregates from a discrete-time process. The underlying discrete-time process is assumed to be a stationary AutoRegressive Fractionally Integrated MovingAverage (ARFIMA) process, after suitable number of differencing if necessary. We derive closed-form expressions for the limiting autocorrelation function and the normal...

We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years....