聚类多元时间序列-有关距离矩阵的问题 [英] Clustering multivariate time series - question regarding distance matrix

问题描述

我正在尝试使用R对气象站进行群集.这些站按小时间隔提供温度，风速，湿度等数据.我可以使用tsclust库轻松地对单变量时间序列进行聚类，但是当我对多变量序列进行聚类时会出现错误.

I am trying to cluster meteorological stations using R. Stations provide such data as temperature, wind speed, humidity and some more on hourly intervals. I can easily cluster univariate time series using tsclust library, but when I cluster multivariate series I get errors.

我有一个数据作为列表，所以每个列表元素都是一个矩阵，其中一个站的时间序列数据(变量是列，行是不同的时间戳).

I have data as a list so each list element is a matrix with time series data of one station (variables are columns and rows are different timestamp).

如果我运行:

tsclust(data, k = 2,
                   distance = 'Euclidean',   seed = 3247, trace = TRUE)

我得到错误:do.call(.External，c(list(CFUN，x，y，pairwise，if(！is.function(method))get(method)else method)，错误，不是标量返回值

I get error: Error in do.call(.External, c(list(CFUN, x, y, pairwise, if (!is.function(method)) get(method) else method), : not a scalar return value

如果尝试使用

dist(data, method="euclidean")

也许无法为此类数据计算欧几里得距离?如果是，那么可以计算出多少距离?

Maybe Euclidean distance can not be calculated for such data? If yes, then what distances could be calculated?

推荐答案

如果您的系列的长度相同，您可以将它们转换为向量，然后重新调整尺寸.但是，就像Anony-Mousse提到的那样，将欧几里得距离与具有不同比例的变量一起使用可能会产生问题，因此考虑使用 zscore 进行标准化:

If your series have the same length, you could just transform them into a vector and then re-adjust dimensions. However, like Anony-Mousse mentioned, using Euclidean distance with variables that have different scales could be problematic, so considering normalizing with zscore:

series <- zscore(data)
pc <- tsclust(lapply(series, as.vector), distance="Euclidean", seed=3247L, trace=TRUE)

pc@datalist <- series
# replace ncol with the actual number of columns from your data
pc@centroids <- lapply(pc@centroids, matrix, ncol=3L)

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