Agri-data analysis is growing rapidly with many parts of the agri-sector using analytics as part of their decision making process. In Ireland, the agri-food sector contributes significant income to the economy and agri-data analytics will become increasingly important in terms of both protecting and expanding this market. However, without a high degree of accuracy, predictions are unusable. Online data for use in analytics has been shown to have significant advantages, mainly due to frequency of updates and to the low cost of data instances. However, agri decision makers must properly interpret fluctuations in data when, for example, they use data mining to forecast prices for their products in the short and medium term. In this work, we present a data mining approach which includes wavelet analysis to provide more accurate analysis of when events which may appear to be outliers, are instead patterns representing events that may occur over the duration of the data stream and then are used for predictions by an ARIMA modelling approach. Our evaluation shows an improvement over more established uses of wavelet analysis in conjunction with ARIMA as we attempt to predict prices using agri-data.