Multidimensional Prediction Models When the Resolution Context Changes

Abstract

Abstract. Multidimensional data is systematically analysed at multiple granularities by applying aggregate and disaggregate operators (e.g., by the use of OLAP tools). For instance, in a supermarket we may want to predict sales of tomatoes for next week, but we may also be interested in predicting sales for all vegetables (higher up in the product hierarchy) for next Friday (lower down in the time dimension). While the domain and data are the same, the operating context is different. We explore several approaches for multidimensional data when predictions have to be made at different levels (or contexts) of aggregation. One method relies on the same resolution, another approach aggregates predictions bottom-up, a third approach disaggregates predictions top-down and a final technique corrects predictions using the relation between levels. We show how these strategies behave when the resolution context changes, using several machine learning techniques in four application domains.

Reframing in context: A systematic approach for model reuse in machine learning

“…Representation change: This category is observed when the attribute representation or their meaning changes from context to context. For instance, some attributes may be merged, or the granularity of the data may change [46] (e.g., a model was built for forecasting sales at city-level but will now need to be adapted to country-level). Task change: A more radical context change is when the task itself changes.…”

“…The three kinds of reframing identified here can be combined in many different ways. For instance, in multidimensional aggregation by means of a data cube [46] both the input variables and the output values are aggregated depending on the operating context (the data cube). Multidimensional approaches are based on hierarchies, and examples and predictions can be aggregated at different levels of the attribute hierarchies, such as the ones shown in Figure 3.…”

“…In the multidimensional datamart example [46] the operating context is an OLAP cube, and θ is not only composed of many parameters but they are also discrete. These cases lead to more complex context plots, but some representations or simplifications may still be insightful.…”