愉快的学习就从翻译开始吧_Multi-step Time Series Forecasting _2_Shampoo Sales Dataset

Shampoo Sales Dataset/洗发水销售数据集

This dataset describes the monthly number of sales of shampoo over a 3-year period.
这个数据集描述了三年期间每月的洗发水销售数字

The units are a sales count and there are 36 observations. The original dataset is credited to Makridakis, Wheelwright, and Hyndman (1998).

这些单位是销售数量，有36个观测值。 原始数据集归功于Makridakis，Wheelwright和Hyndman（1998）。

You can download and learn more about the dataset here.（连接半天打不开，这句话就不关联超链接了，下载地址可以到我这个帖子里找：https://blog.csdn.net/dreamscape9999/article/details/80634644）

The example below loads and creates a plot of the loaded dataset.

下面的例子加载和建立一个加载数据集的图

# load and plot dataset
from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot
# load dataset
def parser(x):
   return datetime.strptime('190'+x, '%Y-%m')
series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# summarize first few rows
print(series.head())
# line plot
series.plot()
pyplot.show()

Running the example loads the dataset as a Pandas Series and prints the first 5 rows.

运行该示例将数据集加载为Pandas序列并打印前5行。

1 2 3 4 5 6 7

Month 1901-01-01    266.0 1901-02-01    145.9 1901-03-01    183.1 1901-04-01    119.3 1901-05-01    180.3 Name: Sales, dtype: float64

A line plot of the series is then created showing a clear increasing trend.

然后创建该系列的线图，显示明显增加的趋势。

Line Plot of Shampoo Sales Dataset

Next, we will take a look at the model configuration and test harness used in the experiment

接下来，我们将看看模型配置和实验中用到的测试工具

（和前面那个单步预测一样的，算是复习吧）