愉快的学习就从翻译开始吧_Multi-step Time Series Forecasting_5_Persistence Model_Make Forecasts

Make Forecasts/进行预测

The next step is to make persistence forecasts.

下一步是进行持续预测

We can implement the persistence forecast easily in a function named persistence() that takes the last observation and the number of forecast steps to persist. This function returns an array containing the forecast.

我们可以用一个叫persistence（）的函数来容易的实现持续预测，他接受最后一个观测值和预测步数来持续（预测）。

1 2 3

# make a persistence forecast def persistence ( last_ob , n_seq ) : return [ last_ob for i in range ( n_seq ) ]

We can then call this function for each time step in the test dataset from December in year 2 to September in year 3.

我们可以为从第二年12月到第三年9月的测试数据集中的每一个时间步调用这个函数

Below is a function make_forecasts() that does this and takes the train, test, and configuration for the dataset as arguments and returns a list of forecasts.

下面是一个函数 make_forecasts()，它来执行此操作，并且它接受train，test，和 configuration for the dataset 作为参数，并且返回一个预测列表

1 2 3 4 5 6 7 8 9 10

# evaluate the persistence model def make_forecasts ( train , test , n_lag , n_seq ) : forecasts = list ( ) for i in range ( len ( test ) ) : X , y = test [ i , 0 : n_lag ] , test [ i , n_lag : ] # make forecast forecast = persistence ( X [ - 1 ] , n_seq ) # store the forecast forecasts . append ( forecast ) return forecasts

We can call this function as follows:

我们可以像下面这样调用这个函数：

1	forecasts = make_forecasts ( train , test , 1 , 3 )