python中的时间序列分析包
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Package for time series analysis in python
提问by foc
I am working on time series in python. The libraries which I found useful and promising are
我正在用 python 处理时间序列。我发现有用且有前途的库是
- pandas;
- statsmodel (for ARIMA);
- simple exponential smoothing is provided from pandas.
- 熊猫；
- 统计模型（用于 ARIMA）；
- pandas 提供了简单的指数平滑。
Also for visualization: matplotlib
也用于可视化：matplotlib
Does anyone know a library for exponential smoothing?
有谁知道指数平滑库？
回答by jseabold
Pandas has exponentially weighted moving moment functions
Pandas 具有指数加权的移动力矩函数
By the way, there shouldn't be any functionality leftover in the scikits.timeseries package that is not also in pandas.
顺便说一句，在 scikits.timeseries 包中不应该有任何剩余的功能，而 Pandas 中也没有。
Edit: Since this is still a popular question, there is now a work in progress pull request to add more fully featured exponential smoothing to statsmodels here
编辑：由于这仍然是一个受欢迎的问题，现在有一个正在进行的拉取请求，以在此处向 statsmodels 添加更多功能齐全的指数平滑
回答by foc
For triple I found on web this http://adorio-research.org/wordpress/?p=1230
对于三重我在网上找到这个http://adorio-research.org/wordpress/?p=1230
回答by RParadox
Somehow some questions got merged or deleted, so I'll post my answer here.
不知何故，有些问题被合并或删除了，所以我会在这里发布我的答案。
Exp smoothing in Python natively.
原生 Python 中的 Exp 平滑。
'''
simple exponential smoothing
go back to last N values
y_t = a * y_t + a * (1-a)^1 * y_t-1 + a * (1-a)^2 * y_t-2 + ... + a*(1-a)^n * y_t-n
'''
from random import random,randint
def gen_weights(a,N):
ws = list()
for i in range(N):
w = a * ((1-a)**i)
ws.append(w)
return ws
def weighted(data,ws):
wt = list()
for i,x in enumerate(data):
wt.append(x*ws[i])
return wt
N = 10
a = 0.5
ws = gen_weights(a,N)
data = [randint(0,100) for r in xrange(N)]
weighted_data = weighted(data,ws)
print 'data: ',data
print 'weights: ',ws
print 'weighted data: ',weighted_data
print 'weighted avg: ',sum(weighted_data)
回答by Chitrasen
you can predict the future values using Pandas Exponentially-weighted moving average http://pandas.pydata.org/pandas-docs/stable/generated/pandas.stats.moments.ewma.htmlas
您可以使用 Pandas 指数加权移动平均线预测未来值http://pandas.pydata.org/pandas-docs/stable/generated/pandas.stats.moments.ewma.html作为
from pandas.stats.moments import ewma
import numpy as np
pred_period = 12
def predict(x,span,periods = pred_period):
x_predict = np.zeros((span+periods,))
x_predict[:span] = x[-span:]
pred = ewma(x_predict,span)[span:]
return pred
