John Moutafi..
7
经过一番搜索,我没有发现任何看起来很有希望作为etspython 的替代品.虽然有一些尝试:StatsModels和pycast的预测方法,您可以检查它们是否适合您的需求.
可用于解决缺少的实现的一个选项是使用子进程模块从python运行R脚本.这里是如何做到这一点很好的文章在这里.
为了做到以后:
您需要创建一个R脚本(例如my_forecast.R),它将
计算(使用ets)并打印文件上的预测,或者stdout(使用cat()命令),以便在脚本运行后使用它们.
您可以从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...)
您还可以为您的参数添加参数,您cmd的Rscript将将其用作命令行参数,如下所示:
args = [arg0, arg1, ...]
cmd = [command, path2script] + args
Then pass cmd to the subprocess
编辑:
我发现了一个示范一系列的霍尔特-温特斯预测文章:第一部分,第2部分和第三部分.除了这些文章中易于理解的分析外,Gregory Trubetskoy(作者)提供了他开发的代码:
初步趋势:
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
最初的季节性成分:
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,您可以将其置于所需的每个项目结构中,并在该项目内的任何位置使用它,只需导入它即可.
我给你赏金,因为你付出了很多努力来回答这个问题,但这仍然不是我想要的.如果将来有人发现(或创建)所有30个Rob Hyndmans状态空间指数平滑模型,我将很乐意制作并奖励另一个赏金:https://www.otexts.org/fpp/7/ 7 (2认同)