35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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import pandas as pd
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import numpy as np
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def time_series_to_supervised(data, n_in=10, n_out=1,dropnan=True):
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"""
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:param data:作为列表或2D NumPy数组的观察序列。需要。
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:param n_in:作为输入的滞后观察数（X）。值可以在[1..len（数据）]之间可选。默认为1。
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:param n_out:作为输出的观测数量（y）。值可以在[0..len（数据）]之间。可选的。默认为1。
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:param dropnan:Boolean是否删除具有NaN值的行。可选的。默认为True。
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:return:
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"""
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n_vars = 1 if type(data) is list else data.shape[1]
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df = pd.DataFrame(data)
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origNames = df.columns
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cols, names = list(), list()
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cols.append(df.shift(0))
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names += [('%s' % origNames[j]) for j in range(n_vars)]
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n_in = max(1, n_in)
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for i in range(n_in-1, 0, -1):
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time = '(t-%d)' % i
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cols.append(df.shift(i))
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names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]
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n_out = max(n_out, 0)
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for i in range(1, n_out+1):
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time = '(t+%d)' % i
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cols.append(df.shift(-i))
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names += [('%s%s' % (origNames[j], time)) for j in range(n_vars)]
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agg = pd.concat(cols, axis=1)
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agg.columns = names
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if dropnan:
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agg.dropna(inplace=True)
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return agg
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