keras: 用time series时间序列做预测_timeseries_dataset_from_array

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知识：数据和标签

【什么是sequence?】

这是一个sequence（一串的历史数据），时间序列预测是由历史数据（蓝色）来推测现在（黑色）。比如假设[ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18]推测20，那么[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]推测30。

这是非紧挨的预测，用昨天的数据推测后天。下面weather forecast的例子：由前720个数据，预测720+72处的标签。sequence长度在采样频率为1时就是720。

预处理：生成时间序列数据集

要生成时间序列数据集，就是由每个data生成sequence，每个sequence对应一个target标签（要预测的值）。

tf.keras.preprocessing.timeseries_dataset_from_array(
    data,
    targets,
    sequence_length,
    sequence_stride=1,
    sampling_rate=1,
    batch_size=128,
    shuffle=False,
    seed=None,
    start_index=None,
    end_index=None,
)
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• data: 表示x数据，里面的每个叫做一个timestep。
• targets: 表示y标签。如果不处理标签只处理数据，传入targets=None。
• sequence_length: 一个输出序列sequence的长度，即有多少个timestep。
• sequence_stride: 每个sequence的开头相隔几个timestep。For stride s, output samples would start at index data[i], data[i + s], data[i + 2 * s], etc.
• sampling_rate: 一个sequence内对timestep的采样频率。For rate r, timesteps data[i], data[i + r], … data[i + sequence_length] are used for create a sample sequence.
• batch_size: 因为返回是tf.data.Dataset，所以要设定分批。

例：只处理一个

data = np.array([i for i in range(100)])    # [0,1,...,99]

timeseries = keras.preprocessing.timeseries_dataset_from_array(
    data,
    targets=None,
    sequence_length=10,
    sampling_rate=2,
    sequence_stride=10
)
print(list(timeseries))
'''
[<tf.Tensor: shape=(9, 10), dtype=int32, numpy=
array([[ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18],
       [10, 12, 14, 16, 18, 20, 22, 24, 26, 28],
       [20, 22, 24, 26, 28, 30, 32, 34, 36, 38],
       [30, 32, 34, 36, 38, 40, 42, 44, 46, 48],
       [40, 42, 44, 46, 48, 50, 52, 54, 56, 58],
       [50, 52, 54, 56, 58, 60, 62, 64, 66, 68],
       [60, 62, 64, 66, 68, 70, 72, 74, 76, 78],
       [70, 72, 74, 76, 78, 80, 82, 84, 86, 88],
       [80, 82, 84, 86, 88, 90, 92, 94, 96, 98]])>]
'''
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注意：[0,1,...,99]的99表示滑动窗口的尾截至到99，而不是滑动窗口的头截至到99。

所以data和targets分开处理是：

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers.experimental import preprocessing

data = np.array([i for i in range(20)])    # [0,1,...,19]
targets = np.array([i for i in range(11)]) # [0,1,...,10]

data_timeseries = keras.preprocessing.timeseries_dataset_from_array(
    data=data,
    targets=None,
    sequence_length=10
)
targets_timeseries = keras.preprocessing.timeseries_dataset_from_array(
    data=targets,
    targets=None,
    sequence_length=1
)
print(list(data_timeseries))
print(list(targets_timeseries))
'''
[<tf.Tensor: shape=(11, 10), dtype=int32, numpy=
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],
       [ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11],
       [ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12],
       [ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13],
       [ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14],
       [ 6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
       [ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16],
       [ 8,  9, 10, 11, 12, 13, 14, 15, 16, 17],
       [ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])>]
[<tf.Tensor: shape=(11, 1), dtype=int32, numpy=
array([[ 0],
       [ 1],
       [ 2],
       [ 3],
       [ 4],
       [ 5],
       [ 6],
       [ 7],
       [ 8],
       [ 9],
       [10]])>]
'''

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例：处理两个

需要填充targets

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers.experimental import preprocessing

data = np.array([i for i in range(20)])    # [0,1,...,19]
targets = np.array([i for i in range(11)])
targets_app = np.zeros(data.size - targets.size, dtype=int)
targets = np.append(targets, targets_app).reshape(data.shape)
print(targets)
# [ 0  1  2  3  4  5  6  7  8  9 10  0  0  0  0  0  0  0  0  0]



timeseries = keras.preprocessing.timeseries_dataset_from_array(
    data,
    targets=targets,
    sequence_length=10
)
print(list(timeseries))

'''
[(<tf.Tensor: shape=(11, 10), dtype=int32, numpy=
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],
       [ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11],
       [ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12],
       [ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13],
       [ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14],
       [ 6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
       [ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16],
       [ 8,  9, 10, 11, 12, 13, 14, 15, 16, 17],
       [ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])>, <tf.Tensor: shape=(11,), dtype=float64, numpy=array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])>)]
'''
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有几点注意：

• 标签targets：targets的shape必须同data的shape一致（targets = np.array([i for i in range(20)])中必须是20）。即使targets不需要那么多（结果是[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]，却传入了[0, ..., 19]）

建立模型

input_shape = (sequence_length, len(selected_features_index))
inputs = keras.layers.Input(shape=input_shape)
lstm_out = keras.layers.LSTM(32)(inputs)
outputs = keras.layers.Dense(1)(lstm_out)
model = keras.Model(inputs=inputs, outputs=outputs)
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每个输入的数据x是一个sequence，一个sequence里有sequence_length个timestep，每个timestep包含len(selected_features_index)个特征。

翻译一下就是，每个输入的数据x是一串各时间的天气数据，有sequence_length个，每个天气数据包含len(selected_features_index)个统计值。

Code

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