愉快的学习就从翻译开始吧_Multi-Step or Sequence Forecasting

Multi-Step or Sequence Forecasting/

A different type of forecasting problem is using past observations to forecast a sequence of future observations.

另一种类型的预测问题是使用过去的观测来预测未来观测的序列。

This may be called sequence forecasting or multi-step forecasting.

这被称为序列预测或多步预测

We can frame a time series for sequence forecasting by specifying another argument. For example, we could frame a forecast problem with an input sequence of 2 past observations to forecast 2 future observations as follows:

我们可以通过指定另一个参数来构建序列预测的时间序列。 例如，我们可以用2个过去的观测值的输入序列来构造预测问题，以预测2个未来的观测值，如下所示：

1	data = series_to_supervised ( values , 2 , 2 )

The complete example is listed below:

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


values = [x for x in range(10)]
data = series_to_supervised(values,2,2)
print(data)

Running the example shows the differentiation of input (t-n) and output (t+n) variables with the current observation (t) considered an output.

运行该示例显示输入（t-n）和输出（t + n）变量与当前观察值（t）被视为输出的差异。

1 2 3 4 5 6 7 8

var1(t-2)  var1(t-1)  var1(t)  var1(t+1) 2        0.0        1.0        2        3.0 3        1.0        2.0        3        4.0 4        2.0        3.0        4        5.0 5        3.0        4.0        5        6.0 6        4.0        5.0        6        7.0 7        5.0        6.0        7        8.0 8        6.0        7.0        8        9.0