DataCanvasIO / HyperTS

A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.
https://hyperts.readthedocs.io
Apache License 2.0
260 stars 27 forks source link

回归预测结果解释 #91

Closed wangjianqiao111 closed 1 year ago

wangjianqiao111 commented 1 year ago

回归任务的预测结果为什么是这样的: 2023-04-06 18:06:55.452 [INFO] [ 5.6191498 7.23569518 7.92735136 6.96889669 6.33656013 2023-04-06 18:06:55.452 [INFO] 5.37992251 5.45399016 5.34192425 5.40060091 7.44965953 2023-04-06 18:06:55.452 [INFO] 10.96693653 12.3248781 13.02884626 14.45617509 11.02101266 2023-04-06 18:06:55.452 [INFO] 9.10442281 7.99758524 7.70621067 11.32062185 14.21717543 2023-04-06 18:06:55.452 [INFO] 18.1368506 22.53652585 30.62719184 29.20028633 30.91615433 2023-04-06 18:06:55.452 [INFO] 30.5234524 26.33733815 24.14380628 22.13217485 20.98701555 2023-04-06 18:06:55.452 [INFO] 23.58383614 26.8686828 30.35203094 39.98220349 42.43875599 2023-04-06 18:06:55.452 [INFO] 41.52651024 41.95603234 47.797445 41.19929391 42.24954343 2023-04-06 18:06:55.452 [INFO] 39.75738478 35.92768741 42.48867488 44.73698974 45.86823166 2023-04-06 18:06:55.452 [INFO] 53.30675328 63.539343 61.51045162 56.02531207 56.48954177 2023-04-06 18:06:55.452 [INFO] 47.25257433 46.55910111 47.89940334 40.01557327 46.1618985 2023-04-06 18:06:55.452 [INFO] 46.36946524 48.28348726 53.69685543 64.69240946 56.14837611 2023-04-06 18:06:55.452 [INFO] 60.04844714 63.29634983 55.59147698 59.00676769 63.52132428 2023-04-06 18:06:55.452 [INFO] 54.23106742 57.85729939 60.8241188 63.46333051 70.38278354 2023-04-06 18:06:55.452 [INFO] 76.20823813 64.26220847 73.16886461 82.56032503 70.24809838 2023-04-06 18:06:55.452 [INFO] 76.18200076 83.97590495 71.64174593 72.25866354 77.46890176 2023-04-06 18:06:55.452 [INFO] 80.64619518 88.7890364 91.77892233 84.62659169 91.9457872 2023-04-06 18:06:55.452 [INFO] 94.74295426 82.61248828 88.74452472 97.46316636 83.61233569 2023-04-06 18:06:55.452 [INFO] 86.65763558 89.22309113 96.04379261 102.11622191 104.23254324 2023-04-06 18:06:55.452 [INFO] 105.02138949 106.86494602 107.72604287 100.8605342 103.81352008 2023-04-06 18:06:55.452 [INFO] 108.50681425 98.57369793 97.44680095 99.72849358 105.24572898 2023-04-06 18:06:55.452 [INFO] 107.04132343 104.9651928 102.46367336 104.54314256 105.07843281 2023-04-06 18:06:55.452 [INFO] 105.42907906 108.08231998 110.84204388 103.60378886 103.21516431 2023-04-06 18:06:55.452 [INFO] 103.09318651 117.38693751 114.92028142 123.25804592 117.35686637 2023-04-06 18:06:55.452 [INFO] 124.84394909 120.21722675 122.92185617 124.42753769 123.32307626 2023-04-06 18:06:55.452 [INFO] 120.24192263 115.64329863 121.77150131 123.98579145 126.13509918 2023-04-06 18:06:55.452 [INFO] 124.70048619 129.48448897 130.35763026 129.30883838 129.8319564 ] 不应该只有一列target列吗

zhangxjohn commented 1 year ago

你的输出没有问题吗?这是一个list吧?

wangjianqiao111 commented 1 year ago

我的target列如下所示: wendu 1 2 3 4 5 6 7 8 ... 145

wangjianqiao111 commented 1 year ago

forecast = model.predict(pre_data) print(forecast) 我直接调的predict方法,出来就是这样的

zhangxjohn commented 1 year ago

这难道不是一列吗?

wangjianqiao111 commented 1 year ago

是吧,就是说我加上列名‘prediction’就是预测结果了

zhangxjohn commented 1 year ago

嗯,是的。

wangjianqiao111 commented 1 year ago

好的,谢谢