愉快的学习就从翻译开始吧_Multi-step Time Series Forecasting_8_Multi-Step LSTM Network_Prepare Data

最新推荐文章于 2023-02-11 19:32:22 发布

翻译最新推荐文章于 2023-02-11 19:32:22 发布 · 528 阅读

本文介绍如何使用LSTM进行多步预测，包括通过差分处理数据的趋势问题，利用MinMaxScaler进行数据缩放，以及如何将数据转换为适合监督学习的形式。

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Multi-Step LSTM Network/多步LSTM网络

In this section, we will use the persistence example as a starting point and look at the changes needed to fit an LSTM to the training data and make multi-step forecasts for the test dataset.

本节，我们将用持续性例子作为起点，看看需要做哪些改变来在训练数据上拟合LSTM,并且在测试数据集上做出多步预测。

Prepare Data/准备数据

The data must be prepared before we can use it to train an LSTM.

数据必须被准备（说处理应该更合适），在用其训练一个LSTM之前

Specifically, two additional changes are required:

具体来说，需要两个额外的更改。

Stationary. The data shows an increasing trend that must be removed by differencing.
固化。数据显示一个增长趋势其必须用差分来消除。（解释的一点都不清楚）
Scale. The scale of the data must be reduced to values between -1 and 1, the activation function of the LSTM units.
比例。数据的比例必须被减小到-1~1之间的值，这是LSTM单元的激活函数（的值的取值范围）

We can introduce a function to make the data stationary called difference(). This will transform the series of values into a series of differences, a simpler representation to work with.

我们可以引入一个函数来使数据固化，称为差分（）。这将把一个值的序列转化为一个差分的序列，一个更简单的表现形式。

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# create a differenced series
def difference(dataset, interval=1):
	diff = list()
	for i in range(interval, len(dataset)):
		value = dataset[i] - dataset[i - interval]
		diff.append(value)
	return Series(diff)

We can use the MinMaxScaler from the sklearn library to scale the data.

我们可以使用sklearn库中的MinMaxScaler来缩放数据。

Putting this together, we can update the prepare_data() function to first difference the data and rescale it, then perform the transform into a supervised learning problem and train test sets as we did before with the persistence example.

综合起来，我们可以更新prepare_data（）函数来首先差分数据并重缩放它，然后执行转换为监督学习问题，并像以前一样使用持续性示例来训练测试集。

The function now returns a scaler in addition to the train and test datasets.

除了训练和测试数据集函数现在还返回一个缩放器。

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# transform series into train and test sets for supervised learning
def prepare_data(series, n_test, n_lag, n_seq):
	# extract raw values
	raw_values = series.values
	# transform data to be stationary
	diff_series = difference(raw_values, 1)
	diff_values = diff_series.values
	diff_values = diff_values.reshape(len(diff_values), 1)
	# rescale values to -1, 1
	scaler = MinMaxScaler(feature_range=(-1, 1))
	scaled_values = scaler.fit_transform(diff_values)
	scaled_values = scaled_values.reshape(len(scaled_values), 1)
	# transform into supervised learning problem X, y
	supervised = series_to_supervised(scaled_values, n_lag, n_seq)
	supervised_values = supervised.values
	# split into train and test sets
	train, test = supervised_values[0:-n_test], supervised_values[-n_test:]
	return scaler, train, test

We can call this function as follows:

我们可以调用这个函数如下：

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# prepare data
scaler, train, test = prepare_data(series, n_test, n_lag, n_seq)