如何解决Python scipy.minimize:double_scalars中遇到溢出并且double_scalars中遇到无效值
我建立了一个自定义的EST(指数平滑)模型。首先,我定义一个函数,其中包含参数定义,这些参数定义将传递给第二个函数,以进行计算并返回预测错误。然后将它们平方并加总。然后,最小化器应优化参数,以使误差平方和最小化。
如果我让函数以起始值运行,则该模型有效。但是,一旦我把它从scipy中最小化了,它就会多次给我带来以下两个错误:
RuntimeWarning:double_scalars中遇到溢出
RuntimeWarning:在double_scalars中遇到无效的值
我检查了数据(y),并且没有零值。因此,计算不应返回任何零。 此外,我尝试了边界和其他方法来最小化这也没有帮助。 (这些是我从其他问题中得到的想法)
非常感谢您的帮助:)
'''
from scipy.optimize import minimize
def model(params,y):
alpha = params[0]
beta = params[1]
gamma = params[2]
omega = params[3]
l_init_HM = params[4]
b_init_HM = params[5]
s_init7_HM = params[6]
s_init6_HM = params[7]
s_init5_HM = params[8]
s_init4_HM = params[9]
s_init3_HM = params[10]
s_init2_HM = params[11]
s_init_HM = params[12]
results = ETS_M_Ad_M(alpha,beta,gamma,omega,l_init_HM,b_init_HM,s_init7_HM,s_init6_HM,s_init5_HM,s_init4_HM,s_init3_HM,s_init2_HM,s_init_HM,y)
error_list = results['errors_list']
error_list = [number ** 2 for number in error_list]
#returning the sum of squared errors
#this is the ML estimate,or rather Adjusted Least Squared (ALS)
#Hyndman p. 69
error_sum = sum(error_list)
return error_sum
def ETS_M_Ad_M(alpha,y):
#computing the number of time points as the length of the forecasting vector
t = len(y)
errors_list = list()
point_forecast = list()
l_list = list()
b_list = list()
s_list = list()
#parameter definition
#Initilaisation
l_past = l_init_HM
b_past = b_init_HM
s_past = s_init7_HM
s_past7 = s_init6_HM
s_past6 = s_init5_HM
s_past5 = s_init4_HM
s_past4 = s_init3_HM
s_past3 = s_init2_HM
s_past2 = s_init_HM
mu = (l_past + omega * b_past) * s_past
#compute forecasting error at timepoint t
e = (y[0] - mu) / y[0]
#compute absolute errors for ML estimation
e_absolute = y[0] - mu
#save estimation error for Likelihood computation
errors_list.append(e_absolute)
point_forecast.append(mu)
l_list.append(l_past)
b_list.append(b_past)
s_list.append(s_past)
#Updating
#updating all state estimates for time point t
l = (l_past + omega * b_past) * (1 + alpha * e)
b = omega * b_past + beta * (l_past + omega * b_past) * e
s = s_past * (1 + gamma * e)
#computation loop:
for i in range(1,t): #start at 1 as the first index '0' is used in the initialization
#Prediciton
#denote updated states from t-1 as past states for time point t
l_past = l
b_past = b
s_past7 = s_past6
s_past6 = s_past5
s_past5 = s_past4
s_past4 = s_past3
s_past3 = s_past2
s_past2 = s
#Observation
#compute one step ahead forecast for timepoint t
mu = (l_past + omega * b_past) * s_past
#compute forecasting error at timepoint t
e = (y[i] - mu) / y[i]
#compute absolute errors for ML estimation
e_absolute = y[i] - mu
#save estimation error for Likelihood computation
#saving squared errors
errors_list.append(e_absolute)
point_forecast.append(mu)
l_list.append(l_past)
b_list.append(b_past)
s_list.append(s_past)
#Updating
#updating all state estimates for time point t
l = (l_past + omega * b_past) * (1 + alpha * e)
b = omega * b_past + beta * (l_past + omega * b_past) * e
s = s_past * (1 + gamma * e)
return {'errors_list' : errors_list,'point forecast' : point_forecast,'l_list' : l_list,'b_list' : b_list,'s_list' : s_list}
#Defining Starting Parameters
Starting_Parameters = [0.1,#alpha
0.01,#beta
0.01,#Gamma
0.99,#omega
5556.151751807499,#l_init
92.90080519198762,#b_init
1.256185460504065,#s_init7
1.0317387565497154,#s_init6
0.8373829313978448,#s_init5
0.8220047728017161,#s_init4
0.8461049900287951,#s_init3
0.9412435736696254,#s_init2
1.2653395150482378] #s_init
# -> starting values from Hyndman 2008 p.24
minimize(model,Starting_Parameters,args=(y),method='BFGS')
'''
y中包含的时间序列通过以下链接上传到我的GitHub: https://github.com/MatthiasHerp/Public/blob/master/revenue_CA_1_FOODS_day.csv
只需将其导入并存储为y,代码便应运行:)
解决方法
阿尔法,贝塔,伽玛和欧米伽不应该限制在0和1之间吗?
您还忘记了在for循环中分配s_past
。
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