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| import matplotlib.pyplot as plt | |
| # 双指数平滑 | |
| def double_exponential_smoothing(series, alpha, beta): | |
| """ | |
| series - dataset with timeseries | |
| alpha - float [0.0, 1.0], smoothing parameter for level | |
| beta - float [0.0, 1.0], smoothing parameter for trend | |
| """ | |
| # first value is same as series | |
| result = [series[0]] | |
| for n in range(1, len(series) + 1): | |
| if n == 1: | |
| level, trend = series[0], series[1] - series[0] | |
| if n >= len(series): # forecasting | |
| value = result[-1] | |
| else: | |
| value = series[n] | |
| last_level, level = level, alpha * value + (1 - alpha) * (level + trend) | |
| trend = beta * (level - last_level) + (1 - beta) * trend | |
| result.append(level + trend) | |
| return result | |
| def plotDoubleExponentialSmoothing(series, alphas, betas): | |
| """ | |
| Plots double exponential smoothing with different alphas and betas | |
| series - dataset with timestamps | |
| alphas - list of floats, smoothing parameters for level | |
| betas - list of floats, smoothing parameters for trend | |
| """ | |
| with plt.style.context('seaborn-white'): | |
| plt.figure(figsize=(13, 5)) | |
| for alpha in alphas: | |
| for beta in betas: | |
| plt.plot(double_exponential_smoothing(series, alpha, beta), | |
| label="Alpha {}, beta {}".format(alpha, beta)) | |
| plt.plot(series.values, label="Actual") | |
| plt.legend(loc="best") | |
| plt.axis('tight') | |
| plt.title("Double Exponential Smoothing") | |
| plt.grid(True) | |
| plotDoubleExponentialSmoothing(data['trend'], alphas=[0.5, 0.3], betas=[0.9, 0.3]) |