+关注
已关注

分类  

暂无分类

标签  

暂无标签

日期归档  

暂无数据

SVD did not converge when using ARIMA with TimeSeriesSplit

发布于2021-03-08 20:18     阅读(765)     评论(0)     点赞(11)     收藏(1)


0

1

2

3

4

I am trying to predict unemployment rate using ARIMA(4,0,3) with sliding window technique(TimeSeriesSplit) and I encountered the problem where SVD did not converge.

Finland_unemployment_rate = array([[ 3.6],
       [ 3. ],
       [ 3.9],
       [ 4.6],
       [ 6.4],
       [ 2.1],
       [ 2.1],
       [ 2.4],
       [ 2.2],
       [ 2.2],
       [ 2.5],
       [ 2.3],
       [ 2.9],
       [ 2.7],
       [ 3.3],
       [ 4.5],
       [ 6.1],
       [ 2.5],
       [ 2.1],
       [ 2.2],
       [ 2.4],
       [ 3. ],
       [ 3.1],
       [ 3.2],
       [ 4.1],
       [ 4.5],
       [ 5.2],
       [ 6.7],
       [ 9.1],
       [ 5.9],
       [ 5.9],
       [ 6.2],
       [ 7.4],
       [ 7.7],
       [ 8. ],
       [ 8.6],
       [10.3],
       [10.2],
       [10.5],
       [11.7],
       [13.5],
       [11.6],
       [10.6],
       [11.5],
       [11.8],
       [12.7],
       [13.2],
       [12.9],
       [14.3],
       [15.6],
       [16.1],
       [17.1],
       [19. ],
       [16.3],
       [14.9],
       [15.6],
       [16.7],
       [16.9],
       [16.5],
       [17.1],
       [18.7],
       [16.9],
       [18.3],
       [18.5],
       [19.9],
       [16.1],
       [15.4],
       [14.6],
       [15.7],
       [15.2],
       [15. ],
       [14.7],
       [16.9],
       [15.5],
       [15. ],
       [16.1],
       [17.1],
       [14.8],
       [14.8],
       [14.7],
       [14.8],
       [14.8],
       [15.1],
       [15.2],
       [16.3],
       [15. ],
       [14.5],
       [14.7],
       [16.9],
       [15.1],
       [13. ],
       [14. ],
       [14.2],
       [13.8],
       [14.1],
       [13.2],
       [14.2],
       [13.4],
       [13.6],
       [14.3],
       [15.4],
       [13.4],
       [10.8],
       [11.1],
       [11.4],
       [11.3],
       [11.4],
       [11.4],
       [12.1],
       [11.8],
       [12.4],
       [12.6],
       [14.6],
       [12. ],
       [10.1],
       [10.2],
       [10.2],
       [10. ],
       [10.1],
       [10.2],
       [11.1],
       [10.8],
       [10.9],
       [11.5],
       [13.3],
       [10.3],
       [ 8.6],
       [ 9. ],
       [ 9.1],
       [ 9.5],
       [ 9.4],
       [ 9.1],
       [10.6],
       [11.3],
       [11.2],
       [11. ],
       [11.9],
       [10.3],
       [ 7.8],
       [ 8.3],
       [ 9.1],
       [ 8.9],
       [ 8.7],
       [ 8.3],
       [ 9.9],
       [ 9.8],
       [ 9.6],
       [10.3],
       [11.3],
       [ 9.3],
       [ 7.6],
       [ 7.8],
       [ 8.7],
       [ 8.3],
       [ 8.8],
       [ 8.1],
       [ 9.9],
       [ 9.4],
       [ 9.5],
       [10.4],
       [11.9],
       [ 9. ],
       [ 7.8],
       [ 8.1],
       [ 8.1],
       [ 8.5],
       [ 8.2],
       [ 8.1],
       [ 9.5],
       [ 9. ],
       [ 9.9],
       [10.4],
       [11.4],
       [ 9.6],
       [ 7.9],
       [ 7.7],
       [ 8. ],
       [ 8.3],
       [ 8.2],
       [ 8.2],
       [ 9.5],
       [ 9. ],
       [ 9.5],
       [10.6],
       [11.6],
       [ 8.9],
       [ 7.8],
       [ 8. ],
       [ 7.2],
       [ 8. ],
       [ 8.1],
       [ 7.7],
       [ 9.8],
       [ 9.2],
       [ 8.5],
       [10. ],
       [10.2],
       [ 8.7],
       [ 7.3],
       [ 7.2],
       [ 7.1],
       [ 7.2],
       [ 8. ],
       [ 7.6],
       [ 8.7],
       [ 8.4],
       [ 8.1],
       [ 8.6],
       [10.1],
       [ 8.1],
       [ 6.6],
       [ 6.9],
       [ 6.8],
       [ 7.2],
       [ 6.7],
       [ 6.4],
       [ 7.6],
       [ 7.5],
       [ 7.7],
       [ 7.2],
       [ 8.5],
       [ 7.4],
       [ 5.9],
       [ 5.9],
       [ 6.4],
       [ 6.2],
       [ 6.1],
       [ 6. ],
       [ 6.8],
       [ 6.4],
       [ 6.8],
       [ 6.2],
       [ 8.8],
       [ 6.8],
       [ 5.2],
       [ 5.6],
       [ 5.9],
       [ 5.8],
       [ 6. ],
       [ 6.1],
       [ 7. ],
       [ 7.6],
       [ 8.3],
       [ 8.8],
       [10.9],
       [ 9.1],
       [ 7.7],
       [ 7.6],
       [ 7.3],
       [ 8.2],
       [ 8.5],
       [ 7.9],
       [ 9.5],
       [ 9.2],
       [ 9.1],
       [ 9.3],
       [10.5],
       [ 8.8],
       [ 7.5],
       [ 7.3],
       [ 7. ],
       [ 7.4],
       [ 7.1],
       [ 7.9],
       [ 8.2],
       [ 8.4],
       [ 9.3],
       [ 8.2],
       [ 9.8],
       [ 8.4],
       [ 6.9],
       [ 6.6],
       [ 6.9],
       [ 7. ],
       [ 6.2],
       [ 7.4],
       [ 7.8],
       [ 7.7],
       [ 8.5],
       [ 8.4],
       [ 9.5],
       [ 7.9],
       [ 6.9],
       [ 7.3],
       [ 7.1],
       [ 6.9],
       [ 7.3],
       [ 6.9],
       [ 8.7],
       [ 8.7],
       [ 9. ],
       [ 8.8],
       [10.8],
       [ 7.8],
       [ 6.6],
       [ 7.1],
       [ 7.6],
       [ 7.4],
       [ 7.9],
       [ 7.9],
       [ 8.5],
       [ 9.1],
       [ 9.5],
       [ 9. ],
       [10.7],
       [ 9.2],
       [ 7. ],
       [ 7.4],
       [ 8.2],
       [ 8.3],
       [ 8.2],
       [ 8.8],
       [ 8.8],
       [10.1],
       [10.3],
       [10.3],
       [11.8],
       [10. ],
       [ 8.4],
       [ 8.3],
       [ 8.4],
       [ 8.7],
       [ 8.2],
       [ 9.2],
       [ 9.3],
       [ 9.4],
       [10.1],
       [ 9.8],
       [10.8],
       [ 9.3],
       [ 7.8],
       [ 7.2],
       [ 7.7],
       [ 8.1],
       [ 8.1],
       [ 7.9],
       [ 9.2],
       [ 9.2],
       [ 9.6],
       [10.2],
       [10.7],
       [ 8.9],
       [ 7.5],
       [ 7.5],
       [ 8. ],
       [ 7.3],
       [ 7.1],
       [ 8.4],
       [ 8.8],
       [ 8.6],
       [ 8.8],
       [ 8.6],
       [ 9.3],
       [ 6.7],
       [ 6.5],
       [ 6.8],
       [ 6.3],
       [ 6.3],
       [ 6.2],
       [ 5.4],
       [ 6.8],
       [ 7.4],
       [ 7. ],
       [ 8. ],
       [ 8.8],
       [ 6.2],
       [ 6. ],
       [ 6.1],
       [ 5.9],
       [ 6.2],
       [ 5.9],
       [ 6. ],
       [ 7.2],
       [ 6.9],
       [ 7.3],
       [ 8.1],
       [10.6],
       [ 7.9],
       [ 7.7],
       [ 7.7],
       [ 7.6],
       [ 7.4]])
X = Finland_UR.Value

tscv = TimeSeriesSplit(n_splits=381, max_train_size= 320)
Finland_pred = []

for train, test in tscv.split(X):
  X_train, X_test = X[train], X[test]
  if len(X_train) == 320 :
    model = ARIMA(X_train, (4,0,3))
    model = model.fit()
    start = len(train)
    end = len(train) + len(test) -1
    pred = model.predict(start= start, end = end)
    Finland_pred.append(pred)

Initially, my max_train_size is 100, but after a couples of iterations, it said SVD did not converge. After that I changed max_trained_size a couple of times more and got to 350 where this error did not happen anymore. I also tried to change ARIMA to (3,0,3) (1,0,3) but the same problem still appeared when I have a low number of max_train_size. When I changed to ARIMA(1,0,1) the model works even with max_train_size = 100 I looked up online and try to normalize it with Max_min_scaler it it also did not work Thus, What is the problem with my model or data here and what can I do about it to keep my max_train_size relatively low?

Or should I simplify the order of ARIMA?

Thank you!


解决方案


暂无回答

0

1

2

3

4

5

6



所属网站分类: 技术文章 > 问答

作者:黑洞官方问答小能手

链接: https://www.pythonheidong.com/blog/article/879994/946ca96294225d672076/

来源: python黑洞网

任何形式的转载都请注明出处,如有侵权 一经发现 必将追究其法律责任

11 0
收藏该文
已收藏

评论内容:(最多支持255个字符)