A novel transfer learning framework for time series forecasting

Recently, many excellent algorithms for time series prediction issues have been proposed, most of which are developed based on the assumption that sufficient training data and testing data under the same distribution are available. However, in reality, time-series data usually exhibit some kind of time-varying characteristic, which may lead to a wide variability between old data and new data. Hence, how to transfer knowledge over a long time span, when addressing time series prediction issues, poses serious challenges. To solve this problem, in this paper, a hybrid algorithm based on transfer learning, Online Sequential Extreme Learning Machine with Kernels (OS-ELMK), and ensemble learning, abbreviated as TrEnOS-ELMK, is proposed, along with its precise mathematic derivation. It aims to make the most of, rather than discard, the adequate long-ago data, and constructs an algorithm framework for transfer learning in time series forecasting, which is groundbreaking. Inspired by the preferable performance of models ensemble, ensemble learning scheme is also incorporated into our proposed algorithm, where the weights of the constituent models are adaptively updated according to their performances on fresh samples. Compared to many existing time series prediction methods, the newly proposed algorithm takes long-ago data into consideration and can effectively leverage the latent knowledge implied in these data for current prediction. In addition, TrEnOS-ELMK naturally inherits merits of both OS-ELMK and ensemble learning due to its incorporation of the two techniques. Experimental results on three synthetic and six real-world datasets demonstrate the effectiveness of the proposed algorithm.