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Four different automated methods for order identi cation have also been found useful in the model identi cation process: (a) PMDL (predictive minimum descriptive length; Rissanen, 1986a); (b) PLS (predictive least squares; Rissanen, 1986b); (c) PLAV (predictive least absolute value; Djuric & Kay, 1992); and (d) PDC (predictive density criterion; Djuric & Kay, 1992) Two additional methods have been considered less useful: (a) AIC (Akaike information criterion; Akaike, 1974); and (b) MDL (minimum descriptive length; Rissanen, 1978; Schwartz, 1978) A recent simulation study that evaluated these six automated procedures (Djuric & Kay, 1992) found that the AIC and MDL tended to overestimate the order of series One drawback of all the automated procedures is that they are not commonly available in standard computer packages.

This is particularly interesting for applications that are not part of the Contextual Personalisation Architecture (in contrast to platform services that are part of that architecture), since complexity is taken away from application development 525 Usage Record Provider The purpose of the Usage Record Provider (URP) is to store user actions annoted by the user context at the time when the action was realised The main objective is to gather data to be used by the learning part of the Recommender The URP stores this information as a set of elementary Usage Records A Usage Record created by the URP is a data structure that stores the behaviour of an entity in a speci c context It must contain all information that will be useful for learning user preferences.

of new minerals or adsorption to the surfaces of pre-existing minerals), a number of solutes which participate in few such reactions can be used as accurate estimators of evapotranspiration. This is true, for instance, of chloride. Except where the local soils or rocks contain the sodium chloride mineral halite, the increase in chloride concentration between rain water and groundwater will simply re ect the amount of evapotranspiration to which the in ltrating water was subjected. If we compare the chloride contents of rainfall and shallow groundwater we can infer how much of the original rainfall was lost to the atmosphere during its progress through the soil zone. For instance, if the local rainwater contained 3 milligrammes of chloride per liter of water (i.e. 3 mg/L Cl), and the local shallow groundwater contains 20 mg/L Cl, then we can calculate the percentage of the original rainwater which was lost to evapotranspiration as follows: % of rain water lost to evapotranspiration = 100 (1 (3/20)) = 85% or, in other words, of the original rainfall landing at the surface only 15% went to form recharge. Given that we are likely to have measurements of rainfall, such evidence can allow us to calculate average recharge rates without recourse to soil moisture budgeting. However, this approach only yields long-term average recharge rates, and cannot provide the daily estimates afforded by soil moisture budgeting. Nevertheless, this chloride ratio method can provide a useful independent check on the annual average rate calculated by summing daily estimates obtained by other means.

The main parameters each record contains are: the time when the action was performed; the context of the user or group in which the action was performed; the identi er of the user, group or application or service to whom this Usage Record is relevant; the action performed; links to ontologies that allow to semantically interpret the other parameters Applications or services can place Usage Records in the URP They do not have to specify the context the URP will itself collect information from Context Providers Usage Records enable the learning process within the learning component of the Recommender From the Usage Records the situations in which the user requests certain services or performed speci c activities may be derived Based on the learned behaviour of the user, the Recommender is then better able to give sensible recommendations to applications 52.

6 Recommendations for Personalisation The previous sections described the various building blocks that enable contextual personalisation, and in particular how context and user information can be stored and managed The output.

Although the overall goal of the model identi cation process seems straightforward that is, to determine the speci c values of the ARIMA (p, d, q) parameters that most parsimoniously describe the time series this can be a very dif cult task in practice because the different model families are mathematically linked For example, a rst-order autoregressive model (1, 0, 0) can also be represented as an in nite order moving averages model (0, 0, ), or this representation can be reversed such that a rst-order moving averages model (0, 0, 1) can also be represented as an in nite-order autoregressive model ( , 0, 0) Illustrations of Alternative Time Series Figure 235 illustrates four different types of models using computer-generated data (N1 = N2 = 60) The rst graph (A) represents an ideal interrupted time series example initially at Level = 5.