Automatic lag selection in time series forecasting using multiple kernel learning

Agus Widodo, Indra Budi, Belawati H. Widjaja

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


This paper reports the feasibility of employing the recent approach on kernel learning, namely the multiple kernel learning (MKL), for time series forecasting to automatically select the optimal lag length or size of sliding windows. MKL is an approach to choose suitable kernels from a given pool of kernels by exploring the combination of multiple kernels. In this paper, we extend the MKL capability to select the optimal size of sliding windows for time series domain by adopting the data integration approach which has been previously studied in the domain of image processing. In this study, each kernel represents the different lengths of time series lag. In addition, we also examine the feasibility of MKL for decomposed time series. We use the dataset from previous time series competitions as our benchmark. Our experimental results indicate that our approaches perform competitively compared to the previous methods using the same dataset. Furthermore, MKL may predict the detrended time series without explicitly computing the seasonality. The advantage of our method is in its ability in automatically selecting the optimal size of sliding windows and finding the pattern of time series.

Original languageEnglish
Pages (from-to)95-110
Number of pages16
JournalInternational Journal of Machine Learning and Cybernetics
Issue number1
Publication statusPublished - 1 Feb 2016


  • Forecast combination
  • Lag selection
  • Multiple kernel learning
  • Sliding window
  • Time series


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