Abstract
In this paper, we use multi-scale stationary wavelet decomposition technique combined with a linear autoregressive model for one-month-ahead monthly sardine catches forecasting off central southern Chile.The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1964 and 30 December 2008. The proposed forecasting strategy is to decompose the raw sardine catches data set into trend component and residual component by using multi-scale stationary wavelet transform. In wavelet domain, both the trend component and the residual component are independently predicted using a linear autoregressive model. Hence, proposed forecaster is the co-addition of two predicted components. We find that the proposed forecasting method achieves a 99% of the explained variance with a reduced parsimonious and high accuracy.
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Rodriguez, N., Rubio, J., Yañez, E. (2011). Wavelet Autoregressive Model for Monthly Sardines Catches Forecasting Off Central Southern Chile. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_78
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DOI: https://doi.org/10.1007/978-3-642-25085-9_78
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