如何将时间序列与 Sklearn OPTICS 算法一起使用？

Question

我正在尝试对时间序列进行聚类。我也想使用 Sklearn OPTICS。在文档中它说输入向量 X 应该有维度 (n_samples,n_features)。我的数组形式为 (n_samples、n_time_stamps、n_features)。下面代码中的示例。

我的问题是如何将 OPTICS 的拟合函数与时间序列一起使用。我知道人们已经将 OPTICS 和 DBSCAN 用于时间序列。我只是不知道他们是如何实施的。任何帮助将不胜感激。

[[[t00, x0], [t01, x01], ... [t0_n_timestamps, x0_n_timestamps]], 
 [[t10, x10], [t11, x11], ... [t1_n_timestamps, x1_n_timestamps]], 
.
.
.
 [[t_n_samples_0, x_n_samples_0], [[t_n_samples_1, x_n_samples_1], ... [t_n_samples_n_timestamps, x_n_samples_n_timestamps]]]

Answer 1

给定以下 np.array 作为输入：

data = np.array([
    [["00:00", 7], ["00:01", 37], ["00:02", 3]],
    [["00:00", 27], ["00:01", 137], ["00:02", 33]],
    [["00:00", 14], ["00:01", 17], ["00:02", 12]],
    [["00:00", 15], ["00:01", 123], ["00:02", 11]],
    [["00:00", 16], ["00:01", 12], ["00:02", 92]],
    [["00:00", 17], ["00:01", 23], ["00:02", 22]],
    [["00:00", 18], ["00:01", 23], ["00:02", 112]],
    [["00:00", 100], ["00:01", 200], ["00:02", 301]],
    [["00:00", 101], ["00:01", 201], ["00:02", 302]],
    [["00:00", 102], ["00:01", 203], ["00:02", 303]],
    [["00:00", 104], ["00:01", 207], ["00:02", 304]]])

我将进行如下处理：

    # save shape info in three separate variables
    x, y, z = data.shape
    # idea from 
    output_arr = np.column_stack((np.repeat(np.arange(x), y), data.reshape(x * y, -1)))
    # create a df out of the arr
    df = pd.DataFrame(output_arr)
    # rename for understandability
    df = df.rename(columns={0: 'index', 1: 'time', 2: 'value'})
    # Change the orientation between rows and columns so that rows
    # that contain time info become columns
    df = df.pivot(index="index", columns="time", values="value")
    df.rename_axis(None, axis=1).reset_index()
    # get columns that refer to specific interval of time series
    temporal_accessors = ["00:00", "00:01", "00:02"]
    # extract data that will be used to carry out clustering
    data_for_clustering = df[temporal_accessors].to_numpy()

    # a set of exemplary params
    params = {
        "xi": 0.05,
        "metric": "euclidean",
        "min_samples": 3
    }
    clusterer = OPTICS(**params)
    fitted = clusterer.fit(data_for_clustering)
    cluster_labels = fitted.labels_
    df["cluster"] = cluster_labels
    # Note: density based algortihms have a notion of the "noise-cluster", which is marked with
    # -1 by sklearn algorithms. That's why starting index is -1 for density based clustering,
    # and 0 otherwise.

对于给定的数据和呈现的参数选择，您将获得以下集群：[0 0 1 0 0 0 0 0 1 1 1]

如何将时间序列与 Sklearn OPTICS 算法一起使用？

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