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Faced with the growing research towards crude oil price fluctuationsinfluential factors following the accelerated development of Internettechnology, accessible data such as Google search volume index are increasinglyquantified and incorporated into forecasting approaches. In this paper, weapply multi-scale data that including both GSVI data and traditional economicdata related to crude oil price as independent variables and propose a newhybrid approach for monthly crude oil price forecasting. This hybrid approach,based on divide and conquer strategy, consists of K-means method, kernelprincipal component analysis and kernel extreme learning machine , whereK-means method is adopted to divide input data into certain clusters, KPCA isapplied to reduce dimension, and KELM is employed for final crude oil priceforecasting. The empirical result can be analyzed from data and method levels.At the data level, GSVI data perform better than economic data in levelforecasting accuracy but with opposite performance in directional forecastingaccuracy because of Herd Behavior, while hybrid data combined their advantagesand obtain best forecasting performance in both level and directional accuracy.At the method level, the approaches with K-means perform better than thosewithout K-means, which demonstrates that divide and conquer strategy caneffectively improve the forecasting performance.

A new hybrid approach for crude oil price forecasting: Evidence from multi-scale data