样本数据如下：

                     pollution  dew  temp   press wnd_dir  wnd_spd  snow  rain
date                                                                          
2010-01-02 00:00:00      129.0  -16  -4.0  1020.0      SE     1.79     0     0
2010-01-02 01:00:00      148.0  -15  -4.0  1020.0      SE     2.68     0     0
2010-01-02 02:00:00      159.0  -11  -5.0  1021.0      SE     3.57     0     0
2010-01-02 03:00:00      181.0   -7  -5.0  1022.0      SE     5.36     1     0
2010-01-02 04:00:00      138.0   -7  -5.0  1022.0      SE     6.25     2     0
                       ...  ...   ...     ...     ...      ...   ...   ...
2014-12-31 19:00:00        8.0  -23  -2.0  1034.0      NW   231.97     0     0
2014-12-31 20:00:00       10.0  -22  -3.0  1034.0      NW   237.78     0     0
2014-12-31 21:00:00       10.0  -22  -3.0  1034.0      NW   242.70     0     0
2014-12-31 22:00:00        8.0  -22  -4.0  1034.0      NW   246.72     0     0
2014-12-31 23:00:00       12.0  -21  -3.0  1034.0      NW   249.85     0     0

[43800 rows x 8 columns]

根据特征dew、temp、press、wnd_dir、wnd_spd、snow、rain预测pollution

如何将有监督的Python学习问题转换成时间序列？

import pandas as pd


def series_to_supervised(data, columns, 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 = pd.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 += [('%s%d(t-%d)' % (columns[j], 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 += [('%s%d(t)' % (columns[j], j + 1)) for j in range(n_vars)]
        else:
            names += [('%s%d(t+%d)' % (columns[j], j + 1, i)) for j in range(n_vars)]
    # put it all together
    agg = pd.concat(cols, axis=1)
    agg.columns = names
    # drop rows with NaN values
    if dropnan:
        clean_agg = agg.dropna()
    return clean_agg


import numpy as np
if __name__ == '__main__':
    values = [x for x in range(10)]
    values = np.array([[25,17,20,18],[13,17,26,11],[22,26,31,19],[18,19,35,46]])
    data = series_to_supervised(values, ['temp','lr','rw','dir'], 2)

将数据处理成LSTM能输入的格式

import pandas as pd
from util import PROCESS_LEVEL1
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OneHotEncoder
from series_to_supervised_learning import series_to_supervised
pd.options.display.expand_frame_repr = False

def data_helper():
    dataset = pd.read_csv(PROCESS_LEVEL1, header=0, index_col=0)
    print(dataset)
    dataset_columns = dataset.columns
    values = dataset.values

    # 对第四列（风向）数据进行编码，也可进行 哑编码处理
    encoder = LabelEncoder()
    values[:, 4] = encoder.fit_transform(values[:, 4])
    values = values.astype('float32')

    # 对数据进行归一化处理, valeus.shape=(, 8),inversed_transform时也需要8列
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled = scaler.fit_transform(values)

    # 将序列数据转化为监督学习数据
    reframed = series_to_supervised(scaled, dataset_columns, 1, 1)

    #print(reframed.columns[[9, 10, 11, 12, 13, 14, 15]])
    # 只考虑当前时刻(t)的前一时刻（t-1）的PM2.5值

    reframed.drop(reframed.columns[[9, 10, 11, 12, 13, 14, 15]], axis=1, inplace=True)

    values = reframed.values

    n_train_hours = 365 * 24
    train = values[:n_train_hours, :]
    test = values[n_train_hours:, :]

    # 监督学习结果划分,test_x.shape = (, 8)
    train_x, train_y = train[:, :-1], train[:, -1]
    test_x, test_y = test[:, :-1], test[:, -1]
    print(type(train_x))
    # 为了在LSTM中应用该数据，需要将其格式转化为3D format，即[Samples, timesteps, features]
    train_X = train_x.reshape((train_x.shape[0], 1, train_x.shape[1]))
    print(train_X.shape)
    test_X = test_x.reshape((test_x.shape[0], 1, test_x.shape[1]))
    return scaler,test_x,train_X,train_y,test_X,test_y

模型预测代码