Seer fish (Scomberomorus sierra) and mullet (Mugil cephalus) are some of the most important marine fishery resources along the Colombian Pacific Ocean. The objective of this study was to forecast the landings of seer fish and mullet based on data from time-series annual landings reported by the Food and Agriculture Organization of the United Nations (FAO) from 1971 to 2014. The study considered autoregressive integrated moving-average (ARIMA) processes to forecast the landings of the species. The ARIMA model (5,1,5) for seer fish and ARIMA model (2,2,1) for mullet showed good agreement concerning the observed data on landings based on the Akaike information criterion. The results show the ARIMA model to be a suitable method for analyzing statistics. In data-poor fisheries situations, this method can support potential evaluations of fishery production for decision making and management.

先知鱼（Scomberomorus sierra）和鱼（Mugil cephalus）是哥伦比亚太平洋沿岸一些最重要的海洋渔业资源。这项研究的目的是根据联合国粮食及农业组织（FAO）从1971年至2014年报告的按时间顺序年度年度登陆量的数据来预测有鱼和鱼的登陆量。该研究考虑了自回归综合移动-平均（ARIMA）过程来预测物种的降落。基于Akaike信息准则，用于观察鱼的ARIMA模型（5,1,5）和用于鱼的ARIMA模型（2,2,1）在登陆数据方面表现出很好的一致性。结果表明，ARIMA模型是一种适合进行统计分析的方法。在渔业数据匮乏的情况下，这种方法可以支持对渔业生产的潜在评估，以用于决策和管理。