我们的作业

任务说明：调研新冠疫情预测与分析系统。使用一个城市的新冠历史数据（自 行网上获取），应用一种合适的人工智能算法构建新冠疫情预测模型。根据构建 好的模型，能根据前10天的新冠数据，预测未来3天的新冠数据。设计一个新冠 疫情预测与分析系统，完成模型的训练，模型的测试（给出测试精度）和模型的
应用等功能。根据调研结果，可以自行设计更具有实用性和创新性的系统功能。 完成系统设计和开发后，请按附件1实验报告模板按要求完成报告内容并以附件 形式提交

数据

中国1.28~3.10各个省份数据

.csv文件打开

主要是发现中文在csv里面怪怪的，考虑省份在数据处理时候没啥卵用就缩写了

hb,3554,4586,5806,7153,9074,11177,13522,16678,19665,22112,24953,27100,29631,31728,33366,48206,51986,54406,56249,58182,59989,61682,62031,62662,63454,64084,64287,64786,65187,65596,65914,66337,66907,67103,67217,67331,67466,67592,67666,67707,67743,67760,67773,67781,67786,67790,67794,67798,67799,67800,67800
gd,277,354,436,535,632,725,813,895,970,1034,1095,1131,1159,1177,1219,1241,1261,1294,1316,1322,1328,1331,1332,1333,1339,1342,1345,1347,1347,1347,1348,1349,1349,1350,1350,1350,1351,1352,1352,1352,1352,1353,1356,1356,1356,1356,1358,1361,1364,1370,1370
hn,206,278,352,422,493,566,675,764,851,914,981,1033,1073,1105,1135,1169,1184,1212,1231,1246,1257,1262,1265,1267,1270,1271,1271,1271,1271,1272,1272,1272,1272,1272,1272,1272,1272,1272,1272,1272,1272,1272,1273,1273,1273,1273,1273,1273,1273,1273,1273
zj,296,428,537,599,661,724,829,895,954,1006,1048,1075,1092,1117,1131,1145,1155,1162,1167,1171,1172,1174,1175,1203,1205,1205,1205,1205,1205,1205,1205,1205,1205,1206,1213,1213,1215,1215,1215,1215,1215,1215,1215,1215,1215,1227,1231,1231,1232,1232,1232
hn,221,277,332,389,463,521,593,661,711,772,803,838,879,912,946,968,988,1001,1004,1006,1007,1008,1010,1011,1013,1016,1016,1016,1016,1017,1017,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018,1018
ah,152,200,237,297,340,408,480,530,591,665,733,779,830,860,889,910,934,950,962,973,982,986,987,988,989,989,989,989,989,989,990,990,990,990,990,990,990,990,990,990,990,990,990,990,990,990,990,990,990,990,990
jx,109,162,240,286,333,391,476,548,600,661,698,740,771,804,844,872,900,913,925,930,933,934,934,934,934,934,934,934,934,934,935,935,935,935,935,935,935,935,935,935,935,935,935,935,935,935,935,935,935,935,935
sd,130,158,184,206,230,259,275,307,347,386,416,444,466,487,497,509,523,532,537,541,543,544,546,749,750,754,755,756,756,756,756,756,758,758,758,758,758,758,758,758,758,758,760,760,760,760,760,760,761,761,761
js,99,129,168,202,236,271,308,341,373,408,439,468,492,515,543,570,593,604,617,626,629,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631,631
cq,147,182,211,247,275,312,344,376,400,415,428,450,473,489,509,525,532,538,547,552,553,553,560,567,572,573,575,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576,576
sc,108,142,177,207,231,254,282,301,321,344,363,386,405,417,436,451,463,470,481,495,508,514,520,525,526,526,527,529,531,534,538,538,538,538,538,538,539,539,539,539,539,539,539,539,539,539,539,539,540,540,540
hlj,38,43,59,80,95,121,155,190,227,277,295,307,331,360,378,395,418,425,445,457,464,470,476,479,479,480,480,480,480,480,480,480,480,480,480,480,481,481,481,481,481,481,482,482,482,482,482,482,482,482,482
bj,102,111,139,168,191,212,228,253,274,297,315,326,337,342,352,366,372,375,380,381,387,393,395,396,399,399,399,400,400,410,410,411,413,414,414,417,418,422,426,428,428,429,435,435,436,437,442,452,456,469,469
sh,96,112,135,169,182,203,219,243,257,277,286,293,299,303,311,315,318,326,328,332,333,333,334,334,335,335,335,336,336,337,337,337,337,337,338,338,339,342,342,342,342,344,344,344,346,352,353,355,358,361,361
hb,48,65,82,96,104,113,126,135,157,172,195,206,218,239,251,265,283,291,300,301,302,306,307,308,309,311,311,311,312,317,318,318,318,318,318,318,318,318,318,318,318,318,318,318,318,318,318,318,318,318,318
fj,82,101,120,144,159,179,194,205,215,224,239,250,261,267,272,279,281,285,287,290,292,293,293,293,293,293,293,294,294,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296,296
gx,58,78,87,100,111,127,139,150,168,171,183,195,210,215,222,222,226,235,237,238,242,244,245,246,249,249,251,252,252,252,252,252,252,252,252,252,252,252,252,252,252,252,252,252,252,252,252,252,253,253,253
sx,56,63,87,101,116,128,142,165,173,184,195,208,213,219,225,229,230,232,236,240,240,242,244,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,245,246,246,246
yn,55,70,83,91,105,114,119,124,133,136,138,141,149,153,154,156,162,168,171,171,172,173,173,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,174,175,176,176,176
hns,43,46,52,60,64,70,80,91,106,117,124,130,138,142,157,157,158,162,162,163,163,163,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168,168
gz,9,12,29,29,38,46,58,64,71,81,89,99,109,127,133,135,140,143,144,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,146,147,146,146
tj,27,31,32,38,48,58,67,69,78,81,88,90,94,104,110,117,120,121,124,125,127,128,131,132,135,135,135,135,135,135,136,136,136,136,136,136,136,136,136,136,136,136,136,136,136,136,136,136,136,136,136
sx,27,35,39,47,56,66,74,81,90,96,104,115,119,122,124,126,126,127,128,129,130,131,131,132,132,132,132,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133,133
ln,39,41,48,63,69,73,77,88,91,96,105,107,108,111,116,116,117,119,121,121,121,121,121,121,121,121,121,121,121,121,121,121,122,122,125,125,125,125,125,125,125,125,125,125,125,125,125,125,125,125,125
jl,9,14,14,17,23,31,42,54,59,65,69,78,80,81,83,84,86,88,89,89,89,90,91,91,91,91,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93,93
gs,24,26,29,35,40,51,55,57,62,67,71,79,83,86,86,87,90,90,90,90,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,91,102,119,120,124,124,125,127,127,129,132,133,133,133,133

湖北的

其他省份的

这是美丽国的数据

确诊	治愈	死亡	日期
5	0	0	2020/1/27
6	0	0	2020/1/31
6	0	0	2020/2/1
8	0	0	2020/2/2
9	0	0	2020/2/3
11	0	0	2020/2/4
12	0	0	2020/2/6
12	0	0	2020/2/10
13	0	0	2020/2/11
13	3	0	2020/2/12
14	3	0	2020/2/13
15	3	0	2020/2/14
15	3	0	2020/2/15
15	3	0	2020/2/16
15	3	0	2020/2/17
15	3	0	2020/2/20
35	3	0	2020/2/22
35	3	0	2020/2/23
35	3	0	2020/2/24
53	3	0	2020/2/25
57	3	0	2020/2/26
60	3	0	2020/2/27
60	3	0	2020/2/28
63	3	0	2020/2/29
63	3	1	2020/3/1
77	3	1	2020/3/2
100	3	6	2020/3/3
122	3	9	2020/3/4
153	3	11	2020/3/5
232	3	14	2020/3/6
324	10	14	2020/3/7
445	10	19	2020/3/8
572	10	21	2020/3/9
704	10	26	2020/3/10
1004	10	31	2020/3/11
1004	10	31	2020/3/12
1635	10	39	2020/3/13
2084	10	41	2020/3/14
2885	10	60	2020/3/15
3700	10	66	2020/3/16
4661	48	90	2020/3/17
6420	74	108	2020/3/18
10259	106	153	2020/3/19
14250	121	208	2020/3/20
19624	147	260	2020/3/21
26997	176	340	2020/3/22
35360	178	473	2020/3/23
46450	178	593	2020/3/24
55243	354	802	2020/3/25
69194	619	1050	2020/3/26
85840	713	1296	2020/3/27
105470	2072	1710	2020/3/28
124686	2612	2191	2020/3/29
143101	4865	2517	2020/3/30
164603	5896	3170	2020/3/31
189633	7136	4081	2020/4/1
216722	8672	5137	2020/4/2
245658	9311	6068	2020/4/3
278537	9920	7163	2020/4/4
312245	15021	8503	2020/4/5
337971	17582	9654	2020/4/6
368449	19919	10993	2020/4/7
399979	22539	12912	2020/4/8
432579	24213	14830	2020/4/9
467184	26522	16736	2020/4/10
501615	29192	18781	2020/4/11
530830	32314	20646	2020/4/12
558526	42018	22146	2020/4/13
583220	44319	23654	2020/4/14
609696	49966	26059	2020/4/15
640014	52772	31002	2020/4/16
672246	56236	33318	2020/4/17
706832	59672	37084	2020/4/18
735366	66854	39095	2020/4/19
760570	71011	40702	2020/4/20
788920	73533	42458	2020/4/21
826184	75682	45150	2020/4/22
843937	76616	46838	2020/4/23
870468	80937	50031	2020/4/24
907096	99121	52063	2020/4/25
940797	105823	54001	2020/4/26
967585	107070	54931	2020/4/27
989357	111587	56386	2020/4/28
1014568	115936	58471	2020/4/29
1040608	124064	61123	2020/4/30
1070032	153947	63019	2020/5/1
1107815	164015	65244	2020/5/2
1133069	175382	66385	2020/5/3
1158341	180152	67686	2020/5/4
1181885	187180	69079	2020/5/5
1206323	189791	71152	2020/5/6
1231943	189910	73566	2020/5/7
1256972	195036	75670	2020/5/8
1286833	198993	77280	2020/5/9
1312099	212534	78862	2020/5/10
1332411	216169	79606	2020/5/11
1351200	232733	80897	2020/5/12
1371395	230287	82461	2020/5/13
1395265	243430	84313	2020/5/14
1419998	246414	85974	2020/5/15
1443397	250747	87568	2020/5/16

一些思路

对于这道题的一些思考

• SIR 是基本的传染病预测模型 构建起来比较简单 考虑到 易感 感染 康复 三者即可 （精度不足）
• SEIR 即SIR 的升级模型 在上面的基础上 加了一层潜伏人群
• 基于LSTM的时间序列模型
• 神经网络算法
• 保底 RNN算法

另外借鉴了guyouyang大佬的一些思路，这是他的GitHub地址。

最后

以上就是傲娇铃铛最近收集整理的关于数据集和误差分析我们的作业数据一些思路的全部内容，更多相关数据集和误差分析我们内容请搜索靠谱客的其他文章。