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