愉快的学习就从翻译开始吧_How to Convert a Time Series to a Supervised Learning Problem in Python_2

气死了，翻译半天，这节弄完了，账号却怎么不在登录状态了？搞得也没保存，废了！这节不翻译了，以后回头再看不懂的时候再翻译！

The series_to_supervised() Function/series_to_supervised()函数

We can use the shift() function in Pandas to automatically create new framings of time series problems given the desired length of input and output sequences.

我们可以用Pandas中的shift()函数

This would be a useful tool as it would allow us to explore different framings of a time series problem with machine learning algorithms to see which might result in better performing models.

In this section, we will define a new Python function named series_to_supervised() that takes a univariate or multivariate time series and frames it as a supervised learning dataset.

The function takes four arguments:

• data: Sequence of observations as a list or 2D NumPy array. Required.
• n_in: Number of lag observations as input (X). Values may be between [1..len(data)] Optional. Defaults to 1.
• n_out: Number of observations as output (y). Values may be between [0..len(data)-1]. Optional. Defaults to 1.
• dropnan: Boolean whether or not to drop rows with NaN values. Optional. Defaults to True.

The function returns a single value:

• return: Pandas DataFrame of series framed for supervised learning.

The new dataset is constructed as a DataFrame, with each column suitably named both by variable number and time step. This allows you to design a variety of different time step sequence type forecasting problems from a given univariate or multivariate time series.

Once the DataFrame is returned, you can decide how to split the rows of the returned DataFrame into X and y components for supervised learning any way you wish.

The function is defined with default parameters so that if you call it with just your data, it will construct a DataFrame with t-1 as X and t as y.

The function is confirmed to be compatible with Python 2 and Python 3.

The complete function is listed below, including function comments.

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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

Can you see obvious ways to make the function more robust or more readable?
Please let me know in the comments below.

Now that we have the whole function, we can explore how it may be used.