DL之GRU：基于2022年6月最新上证指数数据集利用GRU算法预测最新股票上证指数实现回归预测

日期	股票代码	名称	收盘价	最高价	最低价	开盘价	前收盘	涨跌额	涨跌幅	成交量	成交金额
1990/12/19	'000001	上证指数	99.98	99.98	95.79	96.05	None	None	None	1260	494000
1990/12/20	'000001	上证指数	104.39	104.39	99.98	104.3	99.98	4.41	4.4109	197	84000
1990/12/21	'000001	上证指数	109.13	109.13	103.73	109.07	104.39	4.74	4.5407	28	16000
1990/12/24	'000001	上证指数	114.55	114.55	109.13	113.57	109.13	5.42	4.9666	32	31000
1990/12/25	'000001	上证指数	120.25	120.25	114.55	120.09	114.55	5.7	4.976	15	6000
1990/12/26	'000001	上证指数	125.27	125.27	120.25	125.27	120.25	5.02	4.1746	100	53000
1990/12/27	'000001	上证指数	125.28	125.28	125.27	125.27	125.27	0.01	0.008	66	104000
1990/12/28	'000001	上证指数	126.45	126.45	125.28	126.39	125.28	1.17	0.9339	108	88000
1990/12/31	'000001	上证指数	127.61	127.61	126.48	126.56	126.45	1.16	0.9174	78	60000
1991/1/2	'000001	上证指数	128.84	128.84	127.61	127.61	127.61	1.23	0.9639	91	59000
1991/1/3	'000001	上证指数	130.14	130.14	128.84	128.84	128.84	1.3	1.009	141	93000
1991/1/4	'000001	上证指数	131.44	131.44	130.14	131.27	130.14	1.3	0.9989	420	261000
1991/1/7	'000001	上证指数	132.06	132.06	131.45	131.99	131.44	0.62	0.4717	217	141000
1991/1/8	'000001	上证指数	132.68	132.68	132.06	132.62	132.06	0.62	0.4695	2926	1806000
1991/1/9	'000001	上证指数	133.34	133.34	132.68	133.3	132.68	0.66	0.4974	5603	3228000
1991/1/10	'000001	上证指数	133.97	133.97	133.34	133.93	133.34	0.63	0.4725	9990	5399000
1991/1/11	'000001	上证指数	134.6	134.61	134.51	134.61	133.97	0.63	0.4703	13327	7115000
1991/1/14	'000001	上证指数	134.67	135.19	134.11	134.11	134.6	0.07	0.052	12530	6883000
1991/1/15	'000001	上证指数	134.74	134.74	134.19	134.21	134.67	0.07	0.052	1446	1010000
1991/1/16	'000001	上证指数	134.24	134.74	134.14	134.19	134.74	-0.5	-0.3711	509	270000

# 2、数据预处理

# 2.1、数据清洗

# 2.2、时间格式数据标准化

利用strptime()函数，将时间改为%Y-%m-%d格式

# 2.3、定义y_train

y_train shape： (7200,)

# 2.4、构造时序性矩阵数据集：基于y重新设计训练集——符合时序性


data_all_train shape： (7190, 11)
data_all_train 
         label_0    label_1    label_2  ...    label_8    label_9          y
0       99.9800   104.3900   109.1300  ...   127.6100   128.8400   130.1400
1      104.3900   109.1300   114.5500  ...   128.8400   130.1400   131.4400
2      109.1300   114.5500   120.2500  ...   130.1400   131.4400   132.0600
3      114.5500   120.2500   125.2700  ...   131.4400   132.0600   132.6800
4      120.2500   125.2700   125.2800  ...   132.0600   132.6800   133.3400
...         ...        ...        ...  ...        ...        ...        ...
7185  2870.3422  2868.4587  2875.4176  ...  2846.5473  2836.8036  2846.2217
7186  2868.4587  2875.4176  2898.5760  ...  2836.8036  2846.2217  2852.3512
7187  2875.4176  2898.5760  2883.7378  ...  2846.2217  2852.3512  2915.4311
7188  2898.5760  2883.7378  2867.9237  ...  2852.3512  2915.4311  2921.3980
7189  2883.7378  2867.9237  2813.7654  ...  2915.4311  2921.3980  2923.3711
 
[7190 rows x 11 columns]

	label_0	label_1	label_2	label_3	label_4	label_5	label_6	label_7	label_8	label_9	y
0	99.98	104.39	109.13	114.55	120.25	125.27	125.28	126.45	127.61	128.84	130.14
1	104.39	109.13	114.55	120.25	125.27	125.28	126.45	127.61	128.84	130.14	131.44
2	109.13	114.55	120.25	125.27	125.28	126.45	127.61	128.84	130.14	131.44	132.06
3	114.55	120.25	125.27	125.28	126.45	127.61	128.84	130.14	131.44	132.06	132.68
4	120.25	125.27	125.28	126.45	127.61	128.84	130.14	131.44	132.06	132.68	133.34
5	125.27	125.28	126.45	127.61	128.84	130.14	131.44	132.06	132.68	133.34	133.97
6	125.28	126.45	127.61	128.84	130.14	131.44	132.06	132.68	133.34	133.97	134.6
7	126.45	127.61	128.84	130.14	131.44	132.06	132.68	133.34	133.97	134.6	134.67
8	127.61	128.84	130.14	131.44	132.06	132.68	133.34	133.97	134.6	134.67	134.74
9	128.84	130.14	131.44	132.06	132.68	133.34	133.97	134.6	134.67	134.74	134.24
10	130.14	131.44	132.06	132.68	133.34	133.97	134.6	134.67	134.74	134.24	134.25
11	131.44	132.06	132.68	133.34	133.97	134.6	134.67	134.74	134.24	134.25	134.24
12	132.06	132.68	133.34	133.97	134.6	134.67	134.74	134.24	134.25	134.24	134.24
13	132.68	133.34	133.97	134.6	134.67	134.74	134.24	134.25	134.24	134.24	133.72
14	133.34	133.97	134.6	134.67	134.74	134.24	134.25	134.24	134.24	133.72	133.17
15	133.97	134.6	134.67	134.74	134.24	134.25	134.24	134.24	133.72	133.17	132.61
16	134.6	134.67	134.74	134.24	134.25	134.24	134.24	133.72	133.17	132.61	132.05
17	134.67	134.74	134.24	134.25	134.24	134.24	133.72	133.17	132.61	132.05	131.46
18	134.74	134.24	134.25	134.24	134.24	133.72	133.17	132.61	132.05	131.46	130.95
19	134.24	134.25	134.24	134.24	133.72	133.17	132.61	132.05	131.46	130.95	130.44
20	134.25	134.24	134.24	133.72	133.17	132.61	132.05	131.46	130.95	130.44	129.97
21	134.24	134.24	133.72	133.17	132.61	132.05	131.46	130.95	130.44	129.97	129.51

# 2.5、对训练集进行 Z_score标准归一化处理


data_all_tr2arr_mean: 1964.7695519269184
data_all_tr2arr_std: 1068.4654234837196

# 2.6、将训练集的df格式转为tensor格式


train_loader： 
 <torch.utils.data.dataloader.DataLoader object at 0x0000014BB5A68AC8>

# 3、模型训练

# 3.1、模型建立：定义GRU模型、优化器、损失函数

采用GRU+Fully Connected Layer, hidden_size=64

# 3.2、模型训练：及时保存训练过程中的模型


1 tensor(0.3308, grad_fn=<MseLossBackward>)
2 tensor(0.1350, grad_fn=<MseLossBackward>)
3 tensor(0.0127, grad_fn=<MseLossBackward>)
4 tensor(0.0110, grad_fn=<MseLossBackward>)
5 tensor(0.0114, grad_fn=<MseLossBackward>)
6 tensor(0.0099, grad_fn=<MseLossBackward>)
7 tensor(0.0222, grad_fn=<MseLossBackward>)
8 tensor(0.0130, grad_fn=<MseLossBackward>)
9 tensor(0.0150, grad_fn=<MseLossBackward>)
10 tensor(0.0133, grad_fn=<MseLossBackward>)
11 tensor(0.0057, grad_fn=<MseLossBackward>)
12 tensor(0.0163, grad_fn=<MseLossBackward>)
13 tensor(0.0216, grad_fn=<MseLossBackward>)
14 tensor(0.0193, grad_fn=<MseLossBackward>)
15 tensor(0.0333, grad_fn=<MseLossBackward>)
16 tensor(0.0146, grad_fn=<MseLossBackward>)
17 tensor(0.0118, grad_fn=<MseLossBackward>)
18 tensor(0.0052, grad_fn=<MseLossBackward>)
19 tensor(0.0046, grad_fn=<MseLossBackward>)
20 tensor(0.0033, grad_fn=<MseLossBackward>)
21 tensor(0.0078, grad_fn=<MseLossBackward>)
22 tensor(0.0088, grad_fn=<MseLossBackward>)
23 tensor(0.0049, grad_fn=<MseLossBackward>)
24 tensor(0.0085, grad_fn=<MseLossBackward>)
25 tensor(0.0044, grad_fn=<MseLossBackward>)
26 tensor(0.0034, grad_fn=<MseLossBackward>)
27 tensor(0.0050, grad_fn=<MseLossBackward>)
28 tensor(0.0070, grad_fn=<MseLossBackward>)
29 tensor(0.0072, grad_fn=<MseLossBackward>)
30 tensor(0.0065, grad_fn=<MseLossBackward>)
31 tensor(0.0037, grad_fn=<MseLossBackward>)
32 tensor(0.0054, grad_fn=<MseLossBackward>)
33 tensor(0.0033, grad_fn=<MseLossBackward>)
34 tensor(0.0314, grad_fn=<MseLossBackward>)
35 tensor(0.0035, grad_fn=<MseLossBackward>)
36 tensor(0.0063, grad_fn=<MseLossBackward>)
37 tensor(0.0080, grad_fn=<MseLossBackward>)
38 tensor(0.0028, grad_fn=<MseLossBackward>)
39 tensor(0.0068, grad_fn=<MseLossBackward>)
40 tensor(0.0040, grad_fn=<MseLossBackward>)
41 tensor(0.0021, grad_fn=<MseLossBackward>)
42 tensor(0.0031, grad_fn=<MseLossBackward>)
43 tensor(0.0017, grad_fn=<MseLossBackward>)
44 tensor(0.0040, grad_fn=<MseLossBackward>)
45 tensor(0.0025, grad_fn=<MseLossBackward>)
46 tensor(0.0018, grad_fn=<MseLossBackward>)
47 tensor(0.0041, grad_fn=<MseLossBackward>)
48 tensor(0.0025, grad_fn=<MseLossBackward>)
49 tensor(0.0013, grad_fn=<MseLossBackward>)
50 tensor(0.0034, grad_fn=<MseLossBackward>)
51 tensor(0.0014, grad_fn=<MseLossBackward>)
52 tensor(0.0045, grad_fn=<MseLossBackward>)
53 tensor(0.0051, grad_fn=<MseLossBackward>)
54 tensor(0.0036, grad_fn=<MseLossBackward>)
55 tensor(0.0019, grad_fn=<MseLossBackward>)
56 tensor(0.0046, grad_fn=<MseLossBackward>)
57 tensor(0.0032, grad_fn=<MseLossBackward>)
58 tensor(0.0033, grad_fn=<MseLossBackward>)
59 tensor(0.0033, grad_fn=<MseLossBackward>)
60 tensor(0.0025, grad_fn=<MseLossBackward>)
61 tensor(0.0021, grad_fn=<MseLossBackward>)
62 tensor(0.0021, grad_fn=<MseLossBackward>)
63 tensor(0.0036, grad_fn=<MseLossBackward>)
64 tensor(0.0018, grad_fn=<MseLossBackward>)
65 tensor(0.0075, grad_fn=<MseLossBackward>)
66 tensor(0.0074, grad_fn=<MseLossBackward>)
67 tensor(0.0010, grad_fn=<MseLossBackward>)
68 tensor(0.0018, grad_fn=<MseLossBackward>)
69 tensor(0.0039, grad_fn=<MseLossBackward>)
70 tensor(0.0009, grad_fn=<MseLossBackward>)
71 tensor(0.0035, grad_fn=<MseLossBackward>)
72 tensor(0.0035, grad_fn=<MseLossBackward>)
73 tensor(0.0011, grad_fn=<MseLossBackward>)
74 tensor(0.0047, grad_fn=<MseLossBackward>)
75 tensor(0.0020, grad_fn=<MseLossBackward>)
76 tensor(0.0008, grad_fn=<MseLossBackward>)
77 tensor(0.0019, grad_fn=<MseLossBackward>)
78 tensor(0.0019, grad_fn=<MseLossBackward>)
79 tensor(0.0025, grad_fn=<MseLossBackward>)
80 tensor(0.0013, grad_fn=<MseLossBackward>)
81 tensor(0.0023, grad_fn=<MseLossBackward>)
82 tensor(0.0028, grad_fn=<MseLossBackward>)
83 tensor(0.0020, grad_fn=<MseLossBackward>)
84 tensor(0.0017, grad_fn=<MseLossBackward>)
85 tensor(0.0010, grad_fn=<MseLossBackward>)
86 tensor(0.0011, grad_fn=<MseLossBackward>)
87 tensor(0.0048, grad_fn=<MseLossBackward>)
88 tensor(0.0008, grad_fn=<MseLossBackward>)
89 tensor(0.0008, grad_fn=<MseLossBackward>)
90 tensor(0.0015, grad_fn=<MseLossBackward>)
91 tensor(0.0024, grad_fn=<MseLossBackward>)
92 tensor(0.0036, grad_fn=<MseLossBackward>)
93 tensor(0.0030, grad_fn=<MseLossBackward>)
94 tensor(0.0017, grad_fn=<MseLossBackward>)
95 tensor(0.0005, grad_fn=<MseLossBackward>)
96 tensor(0.0014, grad_fn=<MseLossBackward>)
97 tensor(0.0037, grad_fn=<MseLossBackward>)
98 tensor(0.0048, grad_fn=<MseLossBackward>)
99 tensor(0.0022, grad_fn=<MseLossBackward>)
100 tensor(0.0006, grad_fn=<MseLossBackward>)
101 tensor(0.0005, grad_fn=<MseLossBackward>)
102 tensor(0.0027, grad_fn=<MseLossBackward>)
103 tensor(0.0015, grad_fn=<MseLossBackward>)
104 tensor(0.0014, grad_fn=<MseLossBackward>)
105 tensor(0.0029, grad_fn=<MseLossBackward>)
106 tensor(0.0011, grad_fn=<MseLossBackward>)
107 tensor(0.0082, grad_fn=<MseLossBackward>)
108 tensor(0.0017, grad_fn=<MseLossBackward>)
109 tensor(0.0034, grad_fn=<MseLossBackward>)
110 tensor(0.0010, grad_fn=<MseLossBackward>)
111 tensor(0.0015, grad_fn=<MseLossBackward>)
112 tensor(0.0017, grad_fn=<MseLossBackward>)
113 tensor(0.0016, grad_fn=<MseLossBackward>)
114 tensor(0.0006, grad_fn=<MseLossBackward>)
115 tensor(0.0023, grad_fn=<MseLossBackward>)
116 tensor(0.0006, grad_fn=<MseLossBackward>)
117 tensor(0.0018, grad_fn=<MseLossBackward>)
118 tensor(0.0013, grad_fn=<MseLossBackward>)
119 tensor(0.0016, grad_fn=<MseLossBackward>)
120 tensor(0.0007, grad_fn=<MseLossBackward>)
121 tensor(0.0007, grad_fn=<MseLossBackward>)
122 tensor(0.0043, grad_fn=<MseLossBackward>)
123 tensor(0.0038, grad_fn=<MseLossBackward>)
124 tensor(0.0011, grad_fn=<MseLossBackward>)
125 tensor(0.0025, grad_fn=<MseLossBackward>)
126 tensor(0.0013, grad_fn=<MseLossBackward>)
127 tensor(0.0005, grad_fn=<MseLossBackward>)
128 tensor(0.0013, grad_fn=<MseLossBackward>)
129 tensor(0.0021, grad_fn=<MseLossBackward>)
130 tensor(0.0011, grad_fn=<MseLossBackward>)
131 tensor(0.0034, grad_fn=<MseLossBackward>)
132 tensor(0.0022, grad_fn=<MseLossBackward>)
133 tensor(0.0019, grad_fn=<MseLossBackward>)
134 tensor(0.0020, grad_fn=<MseLossBackward>)
135 tensor(0.0009, grad_fn=<MseLossBackward>)
136 tensor(0.0100, grad_fn=<MseLossBackward>)
137 tensor(0.0009, grad_fn=<MseLossBackward>)
138 tensor(0.0012, grad_fn=<MseLossBackward>)
139 tensor(0.0009, grad_fn=<MseLossBackward>)
140 tensor(0.0003, grad_fn=<MseLossBackward>)
141 tensor(0.0007, grad_fn=<MseLossBackward>)
142 tensor(0.0017, grad_fn=<MseLossBackward>)
143 tensor(0.0027, grad_fn=<MseLossBackward>)
144 tensor(0.0149, grad_fn=<MseLossBackward>)
145 tensor(0.0027, grad_fn=<MseLossBackward>)
146 tensor(0.0024, grad_fn=<MseLossBackward>)
147 tensor(0.0013, grad_fn=<MseLossBackward>)
148 tensor(0.0011, grad_fn=<MseLossBackward>)
149 tensor(0.0006, grad_fn=<MseLossBackward>)
150 tensor(0.0008, grad_fn=<MseLossBackward>)
save success! F:\File_Python\……\20220627_models/RNN_GRU_Model_300_150.pkl

# 3.3、对标签数据单独进行归一化


y2arr_normal：
 [-1.74529705 -1.74116964 -1.73673337 ...  1.26852879  1.29623048
  1.32378233]

# 3.4、基于GRU模型预测：基于训练好的GRU模型，预测test数据集

 cut_train_test： 7700 7200

# 3.5、模型评估


cut_train_test： 7700 7400
RNN_GRU_Model_300 R2    value: 0.8737662561295777
RNN_GRU_Model_300 MAE   value: 48.39948391799124
RNN_GRU_Model_300 MSE   value: 3773.501360880409

# 对比真实值VS预测值曲线

相关阅读:
Microsoft SQL Server 图书管理数据库的建立
 OGV内容生产工业化
 【Python】批量提取图片经纬度并写入csv文件
 MySQL-运维篇初识
 javaScript中Number数字类型方法入门
 海底捞逃离小县城
 米尔AM62x核心板，高配价低，AM335x升级首选
 【学习草稿】bert文本分类
 code编译时报错undefined reference to ...
【MySQL 8】Generated Invisible Primary Keys(GIPK)
原文地址：https://blog.csdn.net/qq_41185868/article/details/125493308