The 2012 FUZZ-IEEE conference competition Learning Fuzzy Systems from Data aims to establish the empirical accuracy of fuzzy forecasting algorithms in the domain of prediction of the sales volume of petroleum products. Currently, there are no guidelines or consensus on a best practice methodology. This paper proposes evolving fuzzy linear regression trees (eFT) to extract from both, daily prices and past sales volume data, information of interest to attain accurate forecasts of the next day sales. Essentially, eFT attempts to find spatio-temporal correlations from a historical perspective of competitors' prices and previous sales. A dimension reduction method based on the least angle regression (LARS) algorithm is considered for input variable selection. Computational experiments show that the eFT predictor using LARS is an effective approach to nonlinear time series forecasting providing encouraging results in the competition scenario.