Sliding window-based support vector regression for predicting micrometeorological data
Sliding window-based support vector regression for predicting micrometeorological data
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DOI：
10.1016/j.eswa.2016.04.012
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发表时间：
2016-10-15
影响因子：
8.5
通讯作者：
Mineno, Hiroshi
中科院分区：
文献类型：
--
作者：
Kaneda, Yukimasa;Mineno, Hiroshi
传感器网络技术正变得越来越广泛和复杂，并且具有许多传感器的设备（诸如智能手机和传感器节点）已被广泛使用。由于这些设备更容易积累各种微气象数据，如温度、湿度和风速，因此积累了大量的微气象数据。近年来，人们一直期望这种被称为大数据的巨大数据量将产生新的知识和价值。因此，许多当前应用已经使用数据挖掘技术或机器学习来利用大数据。然而，微气象数据的各种特征之间存在着复杂的相关性，其特征随时间变化也是多种多样的。因此，即使使用最先进的机器学习算法，也难以以低计算复杂度准确地预测微气象数据。在本文中，我们提出了一种新的方法来预测微气象数据，滑动窗口为基础的支持向量回归（SW-SVR），涉及一种新的组合支持向量回归（SVR）和集成学习。为了方便地表示复杂的微气象数据，SW-SVR建立了几个专门为每个代表性的数据组在不同的自然环境，如不同的季节和气候，并改变权重，以聚合的支持向量机动态测试数据的特点。在我们的实验中，我们预测后1小时和6小时的温度，通过使用大尺度微气象数据在东京。因此，无论测试周期、训练周期和预测范围如何，SW-SVR的预测性能始终大于或等于其他一般方法，如SVR、随机森林和梯度提升。同时，与具有较高预测性能的复杂模型相比，SW-SVR显著减少了模型的建立时间。(C)2016作者出版社：Elsevier Ltd
Sensor network technology is becoming more widespread and sophisticated, and devices with many sensors, such as smartphones and sensor nodes, have been used extensively. Since these devices have more easily accumulated various kinds of micrometeorological data, such as temperature, humidity, and wind speed, an enormous amount of micrometeorological data has been accumulated. In recent years, it has been expected that such an enormous amount of data, called big data, will produce novel knowledge and value. Accordingly, many current applications have used data mining technology or machine learning to exploit big data. However, micrometeorological data has a complicated correlation among different features, and its characteristics change variously with time. Therefore, it is difficult to predict micrometeorological data accurately with low computational complexity even if state-of-the-art machine learning algorithms are used. In this paper, we propose a new methodology for predicting micrometeorological data, sliding window-based support vector regression (SW-SVR) that involves a novel combination of support vector regression (SVR) and ensemble learning. To represent complicated micrometeorological data easily, SW-SVR builds several SVRs specialized for each representative data group in various natural environments, such as different seasons and climates, and changes weights to aggregate the SVRs dynamically depending on the characteristics of test data. In our experiment, we predicted the temperature after 1 h and 6 h by using large-scale micrometeorological data in Tokyo. As a result, regardless of testing periods, training periods, and prediction horizons, the prediction performance of SW-SVR was always greater than or equal to other general methods such as SVR, random forest, and gradient boosting. At the same time, SW-SVR reduced the building time remarkably compared with those of complicated models that have high prediction performance. (C) 2016 The Authors. Published by Elsevier Ltd.