【确认Tensorflow版本】
import tensorflow as tf print(tf.__version__) # EXPECTED OUTPUT # 2.0.0【创建合成数据】创建具有季节性、趋势和一些噪声的时间序列。
import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras def plot_series(time, series, format="-", start=0, end=None): plt.plot(time[start:end], series[start:end], format) plt.xlabel("Time") plt.ylabel("Value") plt.grid(True) def trend(time, slope=0): return slope * time def seasonal_pattern(season_time): """Just an arbitrary pattern, you can change it if you wish""" return np.where(season_time < 0.1, np.cos(season_time * 7 * np.pi), 1 / np.exp(5 * season_time)) def seasonality(time, period, amplitude=1, phase=0): """Repeats the same pattern at each period""" season_time = ((time + phase) % period) / period return amplitude * seasonal_pattern(season_time) def noise(time, noise_level=1, seed=None): rnd = np.random.RandomState(seed) return rnd.randn(len(time)) * noise_level time = np.arange(4 * 365 + 1, dtype="float32") baseline = 10 series = trend(time, 0.1) baseline = 10 amplitude = 40 slope = 0.01 noise_level = 2 # Create the series series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude) # Update with noise series += noise(time, noise_level, seed=42) plt.figure(figsize=(10, 6)) plot_series(time, series) plt.show() # EXPECTED OUTPUT # Chart as in the screencast. First should have 5 distinctive 'peaks'现在我们有了时间序列，我们把它分开，这样我们就可以开始预测了
split_time = 1100 time_train = time[:split_time] x_train = series[:split_time] time_valid = time[split_time:] x_valid = series[split_time:] plt.figure(figsize=(10, 6)) plot_series(time_train, x_train) plt.show() plt.figure(figsize=(10, 6)) plot_series(time_valid, x_valid) plt.show() # EXPECTED OUTPUT # Chart WITH 4 PEAKS between 50 and 65 and 3 troughs between -12 and 0 # Chart with 2 Peaks, first at slightly above 60, last at a little more than that, should also have a single trough at about 0【朴素预测】
naive_forecast = series[split_time-1:-1] plt.figure(figsize=(10, 6)) plot_series(time_valid, x_valid) plot_series(time_valid, naive_forecast) # Expected output: Chart similar to above, but with forecast overlay我们把验证证期的开始放大一点:
plt.figure(figsize=(10, 6)) plot_series(time_valid, x_valid, start=0, end=150) plot_series(time_valid, naive_forecast, start=1, end=151) # EXPECTED - Chart with X-Axis from 1100-1250 and Y Axes with series value and projections. Projections should be time stepped 1 unit 'after' series计算验证期预测和预测之间的均方误差和平均绝对误差:
print(keras.metrics.mean_squared_error(x_valid, naive_forecast).numpy()) print(keras.metrics.mean_absolute_error(x_valid, naive_forecast).numpy()) # Expected Output # 19.578304 # 2.6011968这是我们的基线，现在让我们试试移动平均线:
【用移动平均线】
def moving_average_forecast(series, window_size): """Forecasts the mean of the last few values. If window_size=1, then this is equivalent to naive forecast""" forecast = [] for time in range(len(series) - window_size): forecast.append(series[time:time + window_size].mean()) return np.array(forecast) moving_avg = moving_average_forecast(series, 30)[split_time - 30:] plt.figure(figsize=(10, 6)) plot_series(time_valid, x_valid) plot_series(time_valid, moving_avg) # EXPECTED OUTPUT # CHart with time series from 1100->1450+ on X # Time series plotted # Moving average plotted over it均方误差和平均绝对误差：
print(keras.metrics.mean_squared_error(x_valid, moving_avg).numpy()) print(keras.metrics.mean_absolute_error(x_valid, moving_avg).numpy()) # EXPECTED OUTPUT # 65.786224 # 4.3040023这比朴素的预测还要糟糕!移动平均线不能预测趋势或季节性，所以让我们试着通过使用差分来去除它们。因为季节周期是365天，所以我们要用t - 365的值减去t的值。
diff_series = (series[365:] - series[:-365]) diff_time = time[365:] plt.figure(figsize=(10, 6)) plot_series(diff_time, diff_series) plt.show() # EXPECETED OUTPUT: CHart with diffs很好，趋势和季节性似乎消失了，所以现在我们可以使用移动平均线:
diff_moving_avg = moving_average_forecast(diff_series, 50)[split_time-365-50:] plt.figure(figsize=(10, 6)) plot_series(time_valid, diff_series[split_time-365:]) plot_series(time_valid, diff_moving_avg) plt.show() # Expected output. Diff chart from 1100->1450 + # Overlaid with moving average现在让我们通过添加t - 365的过去值来还原趋势和季节性:
print(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_past).numpy()) print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_past).numpy()) # EXPECTED OUTPUT # 8.498155 # 2.327179比朴素的预测要好，很好。然而，这些预测看起来有点太随机了，因为我们只是在添加过去的值，这些值很噪音。让我们使用移动平均过去的值，以消除一些噪音:
diff_moving_avg_plus_smooth_past = moving_average_forecast(series[split_time-370:-360], 10) + diff_moving_avg plt.figure(figsize=(10, 6)) plot_series(time_valid, x_valid) plot_series(time_valid, diff_moving_avg_plus_smooth_past) plt.show() # EXPECTED OUTPUT: # Similar chart to above, but the overlaid projections are much smootherprint(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_smooth_past).numpy()) print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_smooth_past).numpy()) # EXPECTED OUTPUT # 12.527958 # 2.2034433
