在搜索了一下之后，我还没有找到任何一个看起来很有希望作为python的ets替代品。但是有一些尝试：StatsModels和{a2}，您可以检查它们是否适合您的需要。在

解决缺失实现的一个方法是使用subprocess模块从python运行R脚本。有一篇关于如何做到这一点的好文章here。在

为了后面的操作：您需要创建一个R脚本(例如my_forecast.R)，它将

计算(使用ets)和打印文件上或stdout(使用cat()命令)上的预测，以便在脚本之后使用它们

跑。在

可以从python脚本运行R脚本，如下所示：import subprocess

# You need to define the command that will run the Rscript from the subprocess

command = 'Rscript'

path2script = 'path/to/my_forecast.R'

cmd = [command, path2script]

# Option 1: If your script prints to a file

subprocess.run(cmd)

f = open('path/to/created/file', 'r')

(...Do stuff from here...)

# Option 2: If your script prints to stdout

forecasts = subprocess.check_output(cmd, universal_newlines=True)

(...Do stuff from here...)

您还可以向cmd添加参数，Rscript将使用这些参数作为命令行参数，如下所示：args = [arg0, arg1, ...]

cmd = [command, path2script] + args

Then pass cmd to the subprocess

编辑：

我发现了一系列关于霍尔特·温特斯预测的文章：part1，part2和{a7}。除了这些文章中易于理解的分析之外，Gregory Trubetskoy(作者)还提供了他开发的代码：

初始趋势：def initial_trend(series, slen):

sum = 0.0

for i in range(slen):

sum += float(series[i+slen] - series[i]) / slen

return sum / slen

# >>> initial_trend(series, 12)

# -0.7847222222222222

最初的季节性因素：def initial_seasonal_components(series, slen):

seasonals = {}

season_averages = []

n_seasons = int(len(series)/slen)

# compute season averages

for j in range(n_seasons):

season_averages.append(sum(series[slen*j:slen*j+slen])/float(slen))

# compute initial values

for i in range(slen):

sum_of_vals_over_avg = 0.0

for j in range(n_seasons):

sum_of_vals_over_avg += series[slen*j+i]-season_averages[j]

seasonals[i] = sum_of_vals_over_avg/n_seasons

return seasonals

# >>> initial_seasonal_components(series, 12)

# {0: -7.4305555555555545, 1: -15.097222222222221, 2: -7.263888888888888,

# 3: -5.097222222222222, 4: 3.402777777777778, 5: 8.069444444444445,

# 6: 16.569444444444446, 7: 9.736111111111112, 8: -0.7638888888888887,

# 9: 1.902777777777778, 10: -3.263888888888889, 11: -0.7638888888888887}

最后算法：def triple_exponential_smoothing(series, slen, alpha, beta, gamma, n_preds):

result = []

seasonals = initial_seasonal_components(series, slen)

for i in range(len(series)+n_preds):

if i == 0: # initial values

smooth = series[0]

trend = initial_trend(series, slen)

result.append(series[0])

continue

if i >= len(series): # we are forecasting

m = i - len(series) + 1

result.append((smooth + m*trend) + seasonals[i%slen])

else:

val = series[i]

last_smooth, smooth = smooth, alpha*(val-seasonals[i%slen]) + (1-alpha)*(smooth+trend)

trend = beta * (smooth-last_smooth) + (1-beta)*trend

seasonals[i%slen] = gamma*(val-smooth) + (1-gamma)*seasonals[i%slen]

result.append(smooth+trend+seasonals[i%slen])

return result

# # forecast 24 points (i.e. two seasons)

# >>> triple_exponential_smoothing(series, 12, 0.716, 0.029, 0.993, 24)

# [30, 20.34449316666667, 28.410051892109554, 30.438122252647577, 39.466817731253066, ...

您可以将它们放在一个文件中，例如：holtwinters.py在具有以下结构的文件夹中：forecast_folder

|

└── __init__.py

|

└── holtwinters.py

从这里开始，这是一个python模块，您可以将它放在您想要的每个项目结构中，并在该项目的任何位置使用它，只需导入它。在