John Moutafi..
7
經過一番搜尋,我沒有發現任何看起來很有希望作為etspython 的替代品.雖然有一些嘗試:StatsModels和pycast的預測方法,您可以檢查它們是否适合您的需求.
可用于解決缺少的實作的一個選項是使用子程序子產品從python運作R腳本.這裡是如何做到這一點很好的文章在這裡.
為了做到以後:
您需要建立一個R腳本(例如my_forecast.R),它将
計算(使用ets)并列印檔案上的預測,或者stdout(使用cat()指令),以便在腳本運作後使用它們.
您可以從python腳本運作R腳本,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...)
您還可以為您的參數添加參數,您cmd的Rscript将将其用作指令行參數,如下所示:
args = [arg0, arg1, ...]
cmd = [command, path2script] + args
Then pass cmd to the subprocess
編輯:
我發現了一個示範一系列的霍爾特-溫特斯預測文章:第一部分,第2部分和第三部分.除了這些文章中易于了解的分析外,Gregory Trubetskoy(作者)提供了他開發的代碼:
初步趨勢:
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
最初的季節性成分:
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}
最後算法:
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, ...
您可以将它們放在一個檔案中,例如:holtwinters.py在具有以下結構的檔案夾中:
forecast_folder
|
??? __init__.py
|
??? holtwinters.py
從這裡開始,這是一個python子產品,您可以将其置于所需的每個項目結構中,并在該項目内的任何位置使用它,隻需導入它即可.
我給你賞金,因為你付出了很多努力來回答這個問題,但這仍然不是我想要的.如果将來有人發現(或建立)所有30個Rob Hyndmans狀态空間指數平滑模型,我将很樂意制作并獎勵另一個賞金:https://www.otexts.org/fpp/7/ 7 (2認同)