分布式ARIMA模型用于超长时间序列
Distributed ARIMA Models for Ultra-long Time Series
AI总结：
本文提出一种基于MapReduce的分布式ARIMA预测框架，通过组合局部估计器并最小化全局损失函数，实现对超长时间序列的高效准确预测，优于直接拟合方法。
摘要：
为超长时间序列提供预测在各种活动中起着至关重要的作用，例如投资决策、工业生产安排和农场管理。本文开发了一种新颖的分布式预测框架，通过使用行业标准的MapReduce框架来应对与超长时间序列预测相关的挑战。所提出的模型组合方法通过结合从工作节点传递的时间序列模型的局部估计器并最小化全局损失函数，促进了分布式时间序列预测。通过这种方式，我们不再不切实际地假设超长时间序列的数据生成过程（DGP）保持不变，而是仅对跨越较短时间段的子序列的DGP做出假设。我们使用真实数据应用以及数值模拟，研究了所提出方法与自回归积分滑动平均（ARIMA）模型的性能。与直接用ARIMA模型拟合整个数据相比，我们的方法在点预测和预测区间方面都提高了预测准确性和计算效率，尤其是在更长的预测视界上。此外，我们探讨了一些可能影响我们方法预测性能的潜在因素。
英文摘要：
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 using the industry-standard MapReduce framework. The proposed model combination approach facilitates distributed time series forecasting by combining the local estimators of time series 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 approach with AutoRegressive Integrated Moving Average (ARIMA) models using the real data application as well as numerical simulations. Compared to directly fitting the whole data with ARIMA models, our approach results in 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
