In a recent paper, Naidu [1975] has proposed that thereversal intervals of the geomagnetic field for the period 0-76 m.y. are not independent. In fact, the author has fitted a firstorder autoregressive moving average model to the data published by Heirtzler e~ a/. [1968]. This conclusion, if true, is of importance because it suggests that the mechanism governing the reversals of the geomagnetic...

An approach to exponential smoothing that relies on a linear single source of error state space model is outlined. A maximum likelihood method for the estimation of associated smoothing parameters is developed. Commonly used restrictions on the smoothing parameters are rationalised. Issues surrounding model identi…cation and selection are also considered. It is argued that the proposed revised ...

The method developed and described in this paper departs from the traditional time series analysis approach. The starting premise is that a time series can be broken down into a number of characteristic cases, each of which potentially holds the key for indicating the value of the subsequent observation. The case that constitutes the beginning of the forecasting horizon (the reference case) is ...

Automatic forecasts of large numbers of univariate time series are often needed in business. It is common to have over one thousand product lines that need forecasting at least monthly. In these circumstances, an automatic forecasting algorithm is an essential tool. Automatic forecasting algorithms must determine an appropriate time series model, estimate the parameters and compute the forecast...

To investigate the applicability of ARIMA models in wholesale vegetable market models are built taking sales data of one perishable vegetable from Ahmedabad wholesales market in India. It is found that these models can be applied to forecast the demand with Mean Absolute Percentage Error (MAPE) in the range of 30%. This error is acceptable in fresh produce market where the demand and prices are...

Differencing operators of arbitrarily high order can be constructed by interpolating a polynomial through a set of data followed by differentiation of this polynomial and finally evaluation of the polynomial at the point where a derivative approximation is desired. Furthermore, the interpolating polynomial can be constructed from algebraic, trigonometric, or, perhaps exponential polynomials. Th...

A collection of codes (in MATLAB & Fortran 77), and examples, for solving reaction-diffusion equations in one and two space dimensions is presented. In areas of the mathematical community spectral methods are used to remove the stiffness associated with the diffusive terms in a reactiondiffusion model allowing explicit high order timestepping to be used. This is particularly valuable for two (a...

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient...