Adaptive Forecasting Models Research Articles

A short-term adaptive forecasting model for BIFFEX speculation: a Box—Jenkins approach

There are numerous possible advantages to be gained from the accurate prediction of future movements in the Baltic Freight Index (BFI). Because of the difficulties inherent in long-range forecasting, however, the potential for such predictions to provide insight into the future state of the physical dry bulk market is perhaps limited. The greater accuracy of short-term forecasts, on the other hand, facilitates the development of a forecasting model form is justified by the inevitably continuous nature of futures market speculation. Such a model is developed through the application of the Box—Jenkins approach to time series analysis and forecasting. The methodology is presented and the resulting model is evaluated on the basis of objective measures of predictive power and by comparison with alternative forecasting models. Finally, the applicability of the model to the practice of BIFFEX speculation is assessed by judging its performance within a simulated BIFFEX trading environment.

Recent developments in simple adaptive flow forecasting models in Poland

ABSTRACT Real-time flow hydrograph forecasting models have been operational in Poland as a routine service since 1975. A trend in recent years is a shift away from complex conceptual catchment models to simple ones of the storage function type. Another approach has been to forecast the mass flow curve (S-curve) using an ARMA-type model the parameters of which are estimated via a linear Kalman filter algorithm. Examples are shown of the application of the latter approach to models of both the rainfall-runoff category and the channel routing category including estimation of ungauged lateral inflow in the latter models. Previous experience with the application of a linear Kalman filter to flow hydrograph forecasts is discussed. Derivation of the forecast flow hydrograph from the S-curve is done by converting flow volume increments into flow discharges assuming a linear shape for the flow hydrograph within the time step and a trend corresponding to the last observed trend. In the vicinity of the hydrograph pe...

Adaptive Forecasting Models Based on Predictive Distributions

The availability of data regarding variables of interest in forecasting problems is sometimes limited, but experts may possess a great deal of relevant information. The approach taken in this paper involves the development of adaptive forecasting models based on such information. Since the models and their parameters are difficult to consider intuitively, information from the experts is elicited in terms of predictive distributions of the variable to be forecasted. Various models can then be considered, and attempting to “fit” a model to the predictive distributions is analogous to attempting to fit a model to a set of observations. This general approach to forecasting is developed in this paper, and a specific model, a linear model with normally distributed errors, is considered in some detail. An actual application involving the forecasting of fatigue life of asphalt pavements illustrates the development of forecasting models from experts predictive distributions.

AN EMPIRICAL EVALUATION OF CONSTANT AND ADAPTIVE COMPUTER FORECASTING MODELS FOR INVENTORY CONTROL

ABSTRACTThis study compares the performance of one constant model with two adaptive models. MAD and bias were used as criteria for evaluating performance. The results of testing the adaptive models with the constant model were contrary to the expected better performance of the adaptive models; no significant differences were found between the models over known horizontal, trend, and seasonal demand patterns. However, some of the practical advantages of adaptive forecasting models in business suggest their continued use.