Time-series data streams often contain predictive value in the form of unique patterns. While these patterns may be used as leading indicators for event prediction, a lack of prior knowledge of pattern shape and irregularities can render traditional forecasting methods ineffective. The research in this thesis tested a means of predetermining the most effective combination of transformations to be applied to time-series data when training a classifier to predict whether an event will occur at a given time. The transformations tested on provided data streams included subsetting of the data, aggregation over various numbers of data points, testing of different predictive lead times, and converting the data set into a binary set of values. The benefit of the transformations is to reduce the data used for training down to only the most useful pattern containing points and clarify the predictive pattern contained in the set. In addition, the transformations tested significantly reduce the number of features used for classifier training through subsetting and aggregation. The performance benefit of the transformations was tested through creating a series of daily positive/negative event predictions over the span of a test set derived from each provided data stream. A landmarking system was then developed that utilizes the prior results obtained by the system to predetermine a "best fit" transformation to use on a new, untested data stream. Results indicate that the proposed set of transformations consistently result in improved classifier performance over the use of untransformed data values. Landmarking system testing shows that the use of prior knowledge results in selection of a near best fit transformation when using as few as 3 reference transformations.