Complete Example/完成例子
We can put all of these pieces together.
我们可以将所有这些片放在一起
The complete code example for the multi-step persistence forecast is listed below.
多步持续预测的完整代码示例如下：
from pandas import DataFrame from pandas import concat from pandas import read_csv from pandas import datetime from sklearn.metrics import mean_squared_error from math import sqrt from matplotlib import pyplot # date-time parsing function for loading the dataset def parser(x): return datetime.strptime('190' + x, '%Y-%m') # convert time series into supervised learning problem def series_to_supervised(data, n_in=1, n_out=1, dropnan=True): 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 # transform series into train and test sets for supervised learning def prepare_data(series, n_test, n_lag, n_seq): # extract raw values raw_values = series.values raw_values = raw_values.reshape(len(raw_values), 1) # transform into supervised learning problem X, y supervised = series_to_supervised(raw_values, n_lag, n_seq) supervised_values = supervised.values # split into train and test sets train, test = supervised_values[0:-n_test], supervised_values[-n_test:] return train, test # make a persistence forecast def persistence(last_ob, n_seq): return [last_ob for i in range(n_seq)] # 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 # evaluate the RMSE for each forecast time step def evaluate_forecasts(test, forecasts, n_lag, n_seq): for i in range(n_seq): actual = test[:, (n_lag + i)] predicted = [forecast[i] for forecast in forecasts] rmse = sqrt(mean_squared_error(actual, predicted)) print('t+%d RMSE: %f' % ((i + 1), rmse)) # plot the forecasts in the context of the original dataset def plot_forecasts(series, forecasts, n_test): # plot the entire dataset in blue pyplot.plot(series.values) # plot the forecasts in red for i in range(len(forecasts)): off_s = len(series) - n_test + i - 1 off_e = off_s + len(forecasts[i]) + 1 xaxis = [x for x in range(off_s, off_e)] yaxis = [series.values[off_s]] + forecasts[i] pyplot.plot(xaxis, yaxis, color='red') # show the plot pyplot.show() # load dataset series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser) # configure n_lag = 1 n_seq = 3 n_test = 10 # prepare data train, test = prepare_data(series, n_test, n_lag, n_seq) # make forecasts forecasts = make_forecasts(train, test, n_lag, n_seq) # evaluate forecasts evaluate_forecasts(test, forecasts, n_lag, n_seq) # plot forecasts plot_forecasts(series, forecasts, n_test + 2)
Running the example first prints the RMSE for each of the forecasted time steps.
运行这个例子，先打印每一个预测时间步的RMSE
This gives us a baseline of performance on each time step that we would expect the LSTM to outperform.
这给我们了一个每个时间步的性能基准，我们期望LSTM超越表现
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t+1 RMSE: 144.535304
t+2 RMSE: 86.479905
t+3 RMSE: 121.149168
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The plot of the original time series with the multi-step persistence forecasts is also created. The lines connect to the appropriate input value for each forecast.
原始时间序列和多步持续预测的图被创建， 线连接到每个预测的对应输入值
This context shows how naive the persistence forecasts actually are.
这种关联显示了，持续性预测是多么天真！（特么的废话呀，不过代码搞懂，还是有收获的）
Line Plot of Shampoo Sales Dataset with Multi-Step Persistence Forecasts