用双矢量量化方法预测CATS基准
Forecasting the CATS benchmark with the Double Vector Quantization method
摘要：
采用基于SOM算法的双矢量量化方法对CATS竞赛数据集的100个缺失值进行了长期预测。提出的时间序列的分析，估计这种非线性自回归预测方法的自回归部分的维数。在此基础上分析了双矢量量化（DVQ）方法的实验结果，并进行了讨论。由于DVQ方法的特征之一是它能够预测标量以及值的向量，因此进一步观察到达到预测范围所需的迭代预测的数量。该方法的长期稳定性允许获得相当长期的预测范围的可靠值。
英文摘要：
The Double Vector Quantization method, a long-term forecasting method based on the SOM algorithm, has been used to predict the 100 missing values of the CATS competition data set. An analysis of the proposed time series is provided to estimate the dimension of the auto-regressive part of this nonlinear auto-regressive forecasting method. Based on this analysis experimental results using the Double Vector Quantization (DVQ) method are presented and discussed. As one of the features of the DVQ method is its ability to predict scalars as well as vectors of values, the number of iterative predictions needed to reach the prediction horizon is further observed. The method stability for the long term allows obtaining reliable values for a rather long-term forecasting horizon.
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。
