Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle challenges associated with forecasting ultra-long time series by utilizing the industry-standard MapReduce framework. The proposed model combination approach facilitates distributed time series forecasting by combining the local estimators of ARIMA (AutoRegressive Integrated Moving Average) models delivered from worker nodes and minimizing a global loss function. In this way, instead of unrealistically assuming the data generating process (DGP) of an ultra-long time series stays invariant, we make assumptions only on the DGP of subseries spanning shorter time periods. We investigate the performance of the proposed distributed ARIMA models on an electricity demand dataset. Compared to ARIMA models, our approach results in significantly improved forecasting accuracy and computational efficiency both in point forecasts and prediction intervals, especially for longer forecast horizons. Moreover, we explore some potential factors that may affect the forecasting performance of our approach.
翻译:对超长时间序列的预测在投资决策、工业生产安排和农场管理等各种活动中发挥着关键作用。本文件开发了一个新的分布式预测框架,以应对利用工业标准地图生成框架预测超长时间序列的挑战。拟议模型组合法将工人节点提供的ARIMA(自动递减综合移动平均值)模型的当地估计者结合起来,从而便利分配时间序列预测,并最大限度地减少全球损失功能。这样,我们不现实地假定一个超长时间序列的动态数据产生过程(DGP),而只是假设一个超长时间序列的动态预测过程(DGP),涵盖较短的时段。我们调查了电力需求数据集上拟议的分布式ARIMA模型的性能。与ARIMA模型相比,我们的方法在点预测和预测间隔方面,特别是在较长的预测前景方面,大大提高了预测准确性和计算效率。此外,我们探索了可能影响我们方法预测业绩的一些潜在因素。