Univariate and multivariate stochastic modeling of the chemical composition of iron ores in Northern Goa, India |
| |
| Authors: | B K Sahu and P S Raiker |
| |
| Institution: | (1) Department of Earth Sciences, I.I.T., 400076 Bombay, India;(2) Department of Civil Engineering, College of Engineering, 403405 Farmagudi, Goa, India |
| |
| Abstract: | Chemical components such as SiO
2,TiO
2,MnO, P
2
O
5,and especially Fe
2
O
3
of the iron ores of Bicholim Mine, Northern Goa, have been determined for lateral and vertical sections of the mine at equal intervals of 3 and 1 m, respectively, so as to form the spatial (time) series. Univariate stationary models of the type Autoregressive moving average—ARMA (p, q)—were established for each series on the basis of statistical analyses of their auto (acf) and partial auto (pacf) correlation functions. These models were used for forecasting assay values at different lead distances from any pivot. Principles of parsimony simplified all of the candidate ARMA (p, q) models to pure AR (p) models, and the univariate forecasts were significantly improved by multivariate stochastic forecasts. |
| |
| Keywords: | time series spatial series autoregressive models [AR(p)] forecasting multivariate modeling |
| 本文献已被 SpringerLink 等数据库收录！ |
|
