This work proposes a novel algorithm to forecast big data time series. Based on the well-established Pattern Sequence-based Forecasting algorithm, this new approach has two major contributions to the literature. First, the improvement of the original algorithm with respect to the accuracy of predictions, and second, its transformation into the big data context, having reached meaningful results in terms of scalability. The algorithm uses the Apache Spark distributed computation framework and it is a ready-to-use application with few parameters to adjust. Physical and cloud clusters have been used to carry out the experimentation, which consisted in applying the algorithm to real-world data from Uruguay electricity demand.

这项工作提出了一种预测大数据时间序列的新颖算法。基于成熟的基于模式序列的预测算法，这种新方法对文献有两个主要贡献。首先，对原始算法的预测准确性进行了改进，其次，将其转换为大数据上下文，在可伸缩性方面取得了有意义的结果。该算法使用Apache Spark分布式计算框架，它是一个现成的应用程序，几乎不需要调整任何参数。已使用物理和云集群进行实验，包括将算法应用于乌拉圭电力需求的真实数据。