Correlation filters have provided exceptional results in the field of visual object tracking in the past few years. However, these methods typically learn a single filter to be robust to many different appearance changes, which can be challenging. We propose a simple solution to this problem by utilizing an ensemble method of base trackers trained on different temporal windows of the video history. The proposed tracker, called MTCF, exhibits the following features: i) it can be trained using gradient-based convex optimization; ii) it is robust to short-term and long-term changes in visual appearance. MTCF performs on par with or outperforms state-of-the-art trackers on the OTB and the VOT benchmark datasets. We present an extensive analysis of the performance of MTCF on these benchmark datasets.