The blendingof behaviors

The weakness of any model designed to measure a specific trend is that it stands
independently alone. Each model until this point addresses only one aspect of the
time series. In reality, the data will more than likely have other faces. Realizing that
each series is dependent upon different variables and that no one model can be
consistently used to achieve the desired ‘exactness’ needed, a method of blending
behaviors can be constructed. Working on the proviso that all models have the
potential to produce a forecast of future realities with varying degrees of accuracy a
blend approach weighting the working models may be written as:
Blend modelkóa·BM1kòb·BM2kòc·BM3k for k days in the future
where BM1, BM2, BM3 are the predefined behavioral models and a, b, c are the
optimized weightings that historically give the fit of least error as defined in ‘basis of
trend analysis.’ By the nature of the base models it is then valid to presume that, to
some extent, aòbòcB1.