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斜率法
'''
1.做最小二乘拟合，把序列拟合成一条直线;
2.根据直线的斜率k可以得知序列的主要走势：
例如：(1)k > 0.1763 上升 (2) k < -0.1763 下降 (3)其他
3.然后计算序列各点到直线的距离（和方差一样）
设定一个阈值L，统计超过L的点数目，点数目越多说明序列震荡越厉害
'''
import numpy as np
import math
def trendline(data): # 拟合曲线order=1index=[i for i in range(1,len(data)+1)] # x轴坐标coeffs = np.polyfit(index, list(data), order) # 曲线拟合# k = coeffs[0] # 斜率return coeffsdef judge_slope(coeffs, data, degree, shake=1):tan_k = math.tan(degree*math.pi/180) # 注意弧度转化# print(coeffs[0])# print(tan_k)if coeffs[0] >= tan_k:return "上升"elif coeffs[0] <= -tan_k:return "下降"else:return get_shake(coeffs, data, shake)def get_shake(coeffs, data, shake):count = 0for i, d in enumerate(data): # i+1相当于横坐标，从1开始y = np.polyval(coeffs, i+1)count += (y-d)**2# print("count: ",count)if count > shake:return "波动"else:return "平稳"if __name__ == '__main__':data = [10,15,29,30,56,45,41,19,37,48,46]coeffs = trendline(data)res = judge_slope(coeffs, data, degree=1, shake=1)print(res)cox_stuart趋势检验
'''
判断一组数据的趋势是上升还是下降
'''
import scipy.stats as stats
def cox_stuart(list_c,debug=False):lst=list_c.copy()raw_len=len(lst)if raw_len%2==1:del lst[int((raw_len-1)/2)] # 删除中位数c=int(len(lst)/2)n_pos=n_neg=0for i in range(c):diff=lst[i+c]-lst[i]if diff>0:n_pos+=1elif diff<0:n_neg+=1else:continuenum=n_pos+n_negk=min(n_pos,n_neg) # 双边检验print("k: ",k)print("num:",num)p_value=2*stats.binom.cdf(k,num,0.5) # 二项分布if debug:print('fall:%i, rise:%i, p-value:%f'%(n_neg, n_pos, p_value))if n_pos>n_neg and p_value<0.05: # 双边检验return 'increasing'elif n_neg>n_pos and p_value<0.05: # 双边检验return 'decreasing'else:return 'no trend'list_c = [10,15,29,30,56,45,41,19,37,48,46] # 数据量太小，得不到有效结果
res = cos_staut(list_c, True)
print(res)
参考链接
https://wenku.baidu.com/view/cec731b981c758f5f61f6760.html
https://zhuanlan.zhihu.com/p/112703276
https://bbs.csdn.net/topics/350253275
https://blog.csdn.net/speargod/article/details/79939798
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