简单的lstm时间序列预测-已经开源[Keras版本][开源进行中]_lstm预测开源数据集

网上有很多关于lstm时间序列的博客，我从不同博客那里借鉴了许多。

理论知识：

项目已开源上传到gitee上：

时间序列预测仓库: 本项目从简单入门到涉及部分高级预测代码，正在维护中，涉及单变量单步预测，单变量多步预测，以及多变量单步预测，多变量多步预测。 (gitee.com)

1.首先是构建时间序列数据，然后转化为监督学习数据集。

series_to_supervised()函数

from pandas import DataFrame
from pandas import concat
 
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
	"""
	Frame a time series as a supervised learning dataset.
	Arguments:
		data: Sequence of observations as a list or NumPy array.
		n_in: Number of lag observations as input (X).
		n_out: Number of observations as output (y).
		dropnan: Boolean whether or not to drop rows with NaN values.
	Returns:
		Pandas DataFrame of series framed for supervised learning.
	"""
	n_vars = 1 if type(data) is list else data.shape[1]
	df = DataFrame(data)
	cols, names = list(), list()
	# input sequence (t-n, ... t-1)
	for i in range(n_in, 0, -1):
		cols.append(df.shift(i))
		names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
	# forecast sequence (t, t+1, ... t+n)
	for i in range(0, n_out):
		cols.append(df.shift(-i))
		if i == 0:
			names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
		else:
			names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
	# put it all together
	agg = concat(cols, axis=1)
	agg.columns = names
	# drop rows with NaN values
	if dropnan:
		agg.dropna(inplace=True)
	return agg

 2.

Reference

6 种用 LSTM 做时间序列预测的模型结构 - Keras 实现 - 知乎 (zhihu.com)