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Community Blog PostgreSQL: Performing Linear Regression Analysis to Predict Future Data (Part 2)

PostgreSQL: Performing Linear Regression Analysis to Predict Future Data (Part 2)

In this article, the author discusses a practical example to demonstrate future data prediction using linear regression analysis in PostgreSQL.

By Digoal

Background

This blog helps you familiarize with the principle of linear regression and PostgreSQL functions available to support linear regression analysis.

Practical Example

The following figure shows data prediction errors for Kweichow Moutai's stock price using its historical data.

For the next day’s data, the prediction is most accurate.

For the third day’s data, the prediction error increases slightly.

For the data of the fourth and fifth day, the prediction error is significant.

First, import data to a source table.

Note: Import the data in chronological order. Do not reverse the order.

``````06/02/2010 13.49 13.49 12.52 13.03 38670320 1571709568.000
06/03/2010 13.09 13.26 12.69 12.75 27873689 1135419264.000
06/04/2010 12.63 12.99 12.56 12.77 19305447 775373248.000
06/07/2010 12.52 13.13 12.43 13.03 24762597 997748928.000
06/08/2010 12.99 13.02 12.56 12.82 18987054 762023168.000
06/09/2010 12.87 13.90 12.87 13.36 38510441 1623107328.000
06/10/2010 13.37 13.51 13.26 13.39 19669987 823111744.000
06/11/2010 13.46 13.58 13.27 13.37 18622806 783614336.000
06/17/2010 13.48 13.99 13.29 13.31 25604558 1095663744.000
06/18/2010 13.13 13.23 12.37 12.57 24897719 996842496.000
....

create table orig (id int, x numeric);  ``````

Data conversion:

``````1, 13.49
2, 13.09
,.....  ``````

Then, create a sample table.

``````create table tmp (
id int,
x numeric, --自变量
y numeric --因变量
);  ``````

You can set independent variables and dependent variables in various ways.

For example, you can choose yesterday's closing price as the independent variable and today's opening price as the dependent variable.

You can also perform multivariate analysis. For example, take yesterday's closing price and the trading volume as independent variables and today's opening price as the dependent variable.

Generate yesterday’s sample data to predict today’s data.

``````truncate tmp;
insert into tmp
select id,
lag(x,1) over(order by id)
x from orig;  ``````

Generate the sample data of the last two days to predict the next day’s data.

``````create table tmp1 (like tmp);
insert into tmp1
select id,
x+
lag(x,1) over(order by id)
from orig;  ``````

Generate the sample data of the last three days to predict the data of the next two days.

``````create table tmp2 (like tmp);
insert into tmp2
select id,
x+
lag(x,1) over(order by id)+
lag(x,2) over(order by id)
from orig;  ``````

Generate the sample data of the last four days to predict the data of the next three days.

``````create table tmp3 (like tmp);
insert into tmp3
select id,
x+
lag(x,1) over(order by id)+
lag(x,2) over(order by id)+
lag(x,3) over(order by id)
from orig;  ``````

Generate the sample data of the last five days to predict the data of the next four days.

``````create table tmp4 (like tmp);
insert into tmp4
select id,
x+
lag(x,1) over(order by id)+
lag(x,2) over(order by id)+
lag(x,3) over(order by id)+
lag(x,4) over(order by id)
from orig;  ``````

Use the following function to generate predicted data.

``````CREATE OR REPLACE FUNCTION public.check_predict(
IN v_tbl name,       --  样本表名
IN OUT ov integer,   --  校验哪条记录, 倒数第?个值的预测值, 不停迭代, 最后计算所有的实际值和预测值的corr, 选择最佳相关?
OUT v_id int,        --  真实值唯一标识
OUT r_chkv numeric,  --  真实值, 用于校验
OUT p_yv numeric,    --  预测值,因变量
OUT r_xv numeric,    --  自变量,用于预测因变量
OUT dev numeric,     --  误差
OUT v_slope numeric, --  斜率
OUT v_inter numeric, --  截距
OUT v_r2  numeric,   --  相关性
OUT sampcnt int      --  获得最大相关度的样本数
)
RETURNS record
LANGUAGE plpgsql
AS \$function\$
declare
r2_1 numeric := 0; -- 相关性
r2_2 numeric := 0; -- 最大相关性
inter_1 numeric;  --  截距
slope_1 numeric;  --  斜率
inter_2 numeric;  --  最大相关性截距
slope_2 numeric;  --  最大相关性斜率
v_lmt int := 90;  --  使用的最大样本集, 影响预测准确度
v_min int := 5;   --  使用的最小样本数, 影响预测准确度
begin
--自变量 tbl.x
--  因变量 tbl.y

--  筛选最大相关度的样本数, 并记录下储斜率, 截距.
for I in 0..v_lmt
loop
execute \$_\$with t1
(
select row_number() over(order by id desc) as rn ,*
from
(select id,x,y from \$_\$||v_tbl||\$_\$ where x+y is not null order by id desc offset \$1 limit \$2) t
)
select regr_intercept(t1.y,t1.x), regr_slope(t1.y,t1.x), regr_r2(t1.y,t1.x)
from t1
where t1.rn<=\$3 \$_\$
into inter_1,slope_1,r2_1
using ov, v_lmt+v_min, I +v_min;

if r2_1>r2_2 then
inter_2 := inter_1;
slope_2 := slope_1;
r2_2 := r2_1;
sampcnt := I +v_min;
end if;
end loop;

-- 下一个自变量ID, 用于预测因变量
execute \$_\$select id+1 from \$_\$||v_tbl||\$_\$ where x+y is not null order by id desc offset \$1 limit 1\$_\$
into v_id using ov;

--预测值,自变量,真实值, 如果真实值为空, 说明该条记录没有未来的真实记录, 就是要预测的将来值.
execute \$_\$select round(\$_\$||slope_2||'*x+'||inter_2||\$_\$,4), x, y from \$_\$||v_tbl||\$_\$ where id=\$1 \$_\$
into p_yv,r_xv,r_chkv
using v_id;

dev := abs(1-round(p_yv/r_chkv,4));
v_slope := round(slope_2,5);
v_inter := round(inter_2,5);
v_r2 := round(r2_2,5);

return;
end;
\$function\$  ``````

Generate the predicted data and verification data of the last 100 days.

``````create table p1 as select (check_predict('tmp1', I )).* from generate_series(0,100) t( I);
create table p2 as select (check_predict('tmp2', I )).* from generate_series(0,100) t( I);
create table p3 as select (check_predict('tmp3', I )).* from generate_series(0,100) t( I);
create table p4 as select (check_predict('tmp4', I )).* from generate_series(0,100) t( I);  ``````

Method for predicting data of the next four days:

The next day p1

The 2nd day p2-p1

The 3rd day p3-p2

The 4th day p4-p3

Verification method:

``````select
v_id
r_chkv
p_yv
abs(1-round(p_yv/r_chkv,4)) as mis1
r_chkv2
p_yv2
abs(1-round(p_yv2/r_chkv2,4)) as mis2
r_chkv3
p_yv3
abs(1-round(p_yv3/r_chkv3,4)) as mis3
r_chkv4
p_yv4
abs(1-round(p_yv4/r_chkv4,4)) as mis4
from
(
select
p1.v_id
p1.r_chkv
p1.p_yv
lag(p1.r_chkv,1) over(order by p1.v_id desc) as r_chkv2
p2.p_yv-p1.p_yv as p_yv2
lag(p1.r_chkv,2) over(order by p1.v_id desc) as r_chkv3
p3.p_yv-p2.p_yv as p_yv3
lag(p1.r_chkv,3) over(order by p1.v_id desc) as r_chkv4
p4.p_yv-p3.p_yv as p_yv4
from
p1,p2,p3,p4
where p1.v_id=p2.v_id and p1.v_id=p3.v_id and p1.v_id=p4.v_id
) t;  ``````

Verified data:

`````` v_id | r_chkv | p_yv | miss1 | r_chkv2 | p_yv2 | mis2 | r_chkv3 | p_yv3 | miss3 | r_chkv4 | p_yv4 | miss4
+------------------------------------------------+--------------------------------------------------------------------+--------+
1050 | 157.81 | 159.5745 | 0.0112 | | 160.7402 | | | 162.2997 | | | 151.4013 |
1049 | 159.02 | 160.5464 | 0.0096 | 157.81 | 162.1956 | 0.0278 | | 162.3978 | | | 148.0711 |
1048 | 159.87 | 161.9598 | 0.0131 | 159.02 | 162.4292 | 0.0214 | 157.81 | 162.0693 | 0.0270 | | 147.9578 |
1047 | 161.00 | 162.6313 | 0.0101 | 159.87 | 162.1514 | 0.0143 | 159.02 | 161.7551 | 0.0172 | 157.81 | 158.5270 | 0.0045
1046 | 162.74 | 162.0216 | 0.0044 | 161.00 | 161.6165 | 0.0038 | 159.87 | 158.5411 | 0.0083 | 159.02 | 158.4651 | 0.0035
1045 | 162.33 | 161.3257 | 0.0062 | 162.74 | 149.7262 | 0.0800 | 161.00 | 167.0954 | 0.0379 | 159.87 | 161.2083 | 0.0084
1044 | 161.50 | 158.8824 | 0.0162 | 162.33 | 157.8832 | 0.0274 | 162.74 | 160.6849 | 0.0126 | 161.00 | 163.8815 | 0.0179
1043 | 160.92 | 156.8056 | 0.0256 | 161.50 | 159.7366 | 0.0109 | 162.33 | 159.6163 | 0.0167 | 162.74 | 159.0992 | 0.0224
1042 | 156.49 | 158.8599 | 0.0151 | 160.92 | 159.4193 | 0.0093 | 161.50 | 158.9038 | 0.0161 | 162.33 | 159.4251 | 0.0179
1041 | 156.70 | 160.1439 | 0.0220 | 156.49 | 159.0677 | 0.0165 | 160.92 | 159.2684 | 0.0103 | 161.50 | 160.9401 | 0.0035
1040 | 160.71 | 158.8216 | 0.0118 | 156.70 | 158.9957 | 0.0147 | 156.49 | 160.6000 | 0.0263 | 160.92 | 161.9692 | 0.0065
1039 | 159.25 | 158.4342 | 0.0051 | 160.71 | 160.1400 | 0.0035 | 156.70 | 161.7433 | 0.0322 | 156.49 | 161.8723 | 0.0344
1038 | 158.02 | 159.6522 | 0.0103 | 159.25 | 161.4267 | 0.0137 | 160.71 | 149.8541 | 0.0675 | 156.70 | 175.7699 | 0.1217
1037 | 158.47 | 161.3605 | 0.0182 | 158.02 | 161.7616 | 0.0237 | 159.25 | 163.7032 | 0.0280 | 160.71 | 164.0469 | 0.0208
1036 | 160.50 | 161.8688 | 0.0085 | 158.47 | 163.5362 | 0.0320 | 158.02 | 163.9749 | 0.0377 | 159.25 | 167.3141 | 0.0506
1035 | 161.90 | 162.8418 | 0.0058 | 160.50 | 163.8163 | 0.0207 | 158.47 | 167.1359 | 0.0547 | 158.02 | 153.6915 | 0.0274
1034 | 161.49 | 163.9804 | 0.0154 | 161.90 | 166.8222 | 0.0304 | 160.50 | 166.4108 | 0.0368 | 158.47 | 151.0944 | 0.0465
1033 | 163.87 | 165.8788 | 0.0123 | 161.49 | 166.3372 | 0.0300 | 161.90 | 165.9846 | 0.0252 | 160.50 | 164.6662 | 0.0260
1032 | 163.70 | 166.9794 | 0.0200 | 163.87 | 166.0894 | 0.0135 | 161.49 | 164.6576 | 0.0196 | 161.90 | 161.7623 | 0.0009
1031 | 167.65 | 165.4608 | 0.0131 | 163.70 | 164.2976 | 0.0037 | 163.87 | 161.5290 | 0.0143 | 161.49 | 160.9499 | 0.0033
1030 | 165.70 | 163.8841 | 0.0110 | 167.65 | 161.2073 | 0.0384 | 163.70 | 160.5254 | 0.0194 | 163.87 | 158.8926 | 0.0304
1029 | 164.70 | 160.9278 | 0.0229 | 165.70 | 159.7672 | 0.0358 | 167.65 | 158.2872 | 0.0558 | 163.70 | 158.4474 | 0.0321
1028 | 162.58 | 158.7257 | 0.0237 | 164.70 | 157.5536 | 0.0434 | 165.70 | 157.6546 | 0.0486 | 167.65 | 173.5841 | 0.0354
1027 | 158.81 | 157.3449 | 0.0092 | 162.58 | 157.0147 | 0.0342 | 164.70 | 158.5247 | 0.0375 | 165.70 | 171.3281 | 0.0340
1026 | 158.20 | 156.2507 | 0.0123 | 158.81 | 157.7433 | 0.0067 | 162.58 | 158.9512 | 0.0223 | 164.70 | 160.1243 | 0.0278
1025 | 156.00 | 157.0983 | 0.0070 | 158.20 | 158.4975 | 0.0019 | 158.81 | 159.6796 | 0.0055 | 162.58 | 145.3973 | 0.1057
1024 | 156.00 | 158.3550 | 0.0151 | 156.00 | 159.4457 | 0.0221 | 158.20 | 160.4796 | 0.0144 | 158.81 | 148.8340 | 0.0628
1023 | 157.72 | 159.1382 | 0.0090 | 156.00 | 160.2608 | 0.0273 | 156.00 | 162.2360 | 0.0400 | 158.20 | 146.8649 | 0.0717
1022 | 158.50 | 159.9296 | 0.0090 | 157.72 | 161.8630 | 0.0263 | 156.00 | 160.3253 | 0.0277 | 156.00 | 161.2206 | 0.0335
1021 | 159.27 | 161.3250 | 0.0129 | 158.50 | 160.2559 | 0.0111 | 157.72 | 161.1143 | 0.0215 | 156.00 | 163.1739 | 0.0460
1020 | 160.00 | 160.7364 | 0.0046 | 159.27 | 160.7603 | 0.0094 | 158.50 | 162.8499 | 0.0274 | 157.72 | 163.6353 | 0.0375
1019 | 162.00 | 159.5627 | 0.0150 | 160.00 | 161.9158 | 0.0120 | 159.27 | 163.1346 | 0.0243 | 158.50 | 163.7176 | 0.0329
1018 | 158.74 | 161.2400 | 0.0157 | 162.00 | 162.5665 | 0.0035 | 160.00 | 163.4097 | 0.0213 | 159.27 | 164.0869 | 0.0302
1017 | 159.75 | 162.5560 | 0.0176 | 158.74 | 163.0135 | 0.0269 | 162.00 | 163.6795 | 0.0104 | 160.00 | 164.2167 | 0.0264
1016 | 162.00 | 162.5926 | 0.0037 | 159.75 | 163.1884 | 0.0215 | 158.74 | 163.7584 | 0.0316 | 162.00 | 164.8763 | 0.0178
1015 | 162.20 | 162.5296 | 0.0020 | 162.00 | 163.0662 | 0.0066 | 159.75 | 164.4497 | 0.0294 | 158.74 | 164.8095 | 0.0382
1014 | 162.02 | 162.4474 | 0.0026 | 162.20 | 163.5357 | 0.0082 | 162.00 | 164.2277 | 0.0138 | 159.75 | 166.0446 | 0.0394
1013 | 162.04 | 162.6981 | 0.0041 | 162.02 | 163.3388 | 0.0081 | 162.20 | 162.3852 | 0.0011 | 162.00 | 170.6363 | 0.0533
1012 | 161.81 | 162.8286 | 0.0063 | 162.04 | 164.2343 | 0.0135 | 162.02 | 160.1472 | 0.0116 | 162.20 | 174.4763 | 0.0757
1011 | 162.48 | 163.1765 | 0.0043 | 161.81 | 165.7026 | 0.0241 | 162.04 | 157.7701 | 0.0264 | 162.02 | 176.5230 | 0.0895
1010 | 161.96 | 164.7836 | 0.0174 | 162.48 | 165.9001 | 0.0210 | 161.81 | 166.6854 | 0.0301 | 162.04 | 165.4803 | 0.0212
1009 | 163.10 | 165.4185 | 0.0142 | 161.96 | 165.6852 | 0.0230 | 162.48 | 152.0170 | 0.0644 | 161.81 | 176.6369 | 0.0916
1008 | 164.80 | 164.4899 | 0.0019 | 163.10 | 163.5277 | 0.0026 | 161.96 | 161.2467 | 0.0044 | 162.48 | 166.5030 | 0.0248
1007 | 164.00 | 162.4525 | 0.0094 | 164.80 | 161.3928 | 0.0207 | 163.10 | 168.8513 | 0.0353 | 161.96 | 164.6355 | 0.0165
1006 | 163.00 | 159.8954 | 0.0190 | 164.00 | 162.0882 | 0.0117 | 164.80 | 172.2702 | 0.0453 | 163.10 | 164.4402 | 0.0082
1005 | 160.17 | 160.1776 | 0.0000 | 163.00 | 164.6717 | 0.0103 | 164.00 | 165.7490 | 0.0107 | 164.80 | 155.6993 | 0.0552
1004 | 158.29 | 163.8475 | 0.0351 | 160.17 | 166.8448 | 0.0417 | 163.00 | 168.3659 | 0.0329 | 164.00 | 143.8282 | 0.1230
1003 | 160.74 | 166.3904 | 0.0352 | 158.29 | 163.5572 | 0.0333 | 160.17 | 175.3659 | 0.0949 | 163.00 | 172.4586 | 0.0580
1002 | 164.30 | 166.5811 | 0.0139 | 160.74 | 164.5718 | 0.0238 | 158.29 | 166.3753 | 0.0511 | 160.17 | 182.3603 | 0.1385
1001 | 164.23 | 166.7825 | 0.0155 | 164.30 | 164.0588 | 0.0015 | 160.74 | 167.1305 | 0.0398 | 158.29 | 165.5798 | 0.0461
1000 | 164.00 | 166.1178 | 0.0129 | 164.23 | 165.1107 | 0.0054 | 164.30 | 166.9978 | 0.0164 | 160.74 | 164.5353 | 0.0236
999 | 163.76 | 163.8419 | 0.0005 | 164.00 | 167.2181 | 0.0196 | 164.23 | 162.5489 | 0.0102 | 164.30 | 165.9863 | 0.0103
998 | 162.00 | 163.2600 | 0.0078 | 163.76 | 159.6280 | 0.0252 | 164.00 | 166.3823 | 0.0145 | 164.23 | 167.9779 | 0.0228
997 | 159.60 | 159.9979 | 0.0025 | 162.00 | 159.8471 | 0.0133 | 163.76 | 167.4848 | 0.0227 | 164.00 | 172.3491 | 0.0509
996 | 160.39 | 152.9068 | 0.0467 | 159.60 | 163.1250 | 0.0221 | 162.00 | 161.9731 | 0.0002 | 163.76 | 172.0482 | 0.0506
995 | 153.59 | 152.9868 | 0.0039 | 160.39 | 149.1597 | 0.0700 | 159.60 | 161.8337 | 0.0140 | 162.00 | 148.5907 | 0.0828
994 | 154.09 | 150.5045 | 0.0233 | 153.59 | 144.0137 | 0.0623 | 160.39 | 142.0351 | 0.1144 | 159.60 | 158.6477 | 0.0060
993 | 153.85 | 144.8516 | 0.0585 | 154.09 | 142.1239 | 0.0777 | 153.59 | 130.1523 | 0.1526 | 160.39 | 143.8730 | 0.1030
992 | 149.11 | 141.3053 | 0.0523 | 153.85 | 140.7890 | 0.0849 | 154.09 | 169.4041 | 0.0994 | 153.59 | 122.2508 | 0.2040
991 | 141.87 | 140.9553 | 0.0064 | 149.11 | 141.0677 | 0.0539 | 153.85 | 151.3856 | 0.0160 | 154.09 | 138.8321 | 0.0990
990 | 141.47 | 141.1875 | 0.0020 | 141.87 | 142.3318 | 0.0033 | 149.11 | 144.5009 | 0.0309 | 153.85 | 144.1629 | 0.0630
989 | 141.12 | 142.5450 | 0.0101 | 141.47 | 141.9629 | 0.0035 | 141.87 | 141.8605 | 0.0001 | 149.11 | 140.3056 | 0.0590
988 | 142.00 | 142.9206 | 0.0065 | 141.12 | 142.4254 | 0.0093 | 141.47 | 141.1854 | 0.0020 | 141.87 | 141.4228 | 0.0032
987 | 144.10 | 142.5783 | 0.0106 | 142.00 | 145.0599 | 0.0215 | 141.12 | 137.6055 | 0.0249 | 141.47 | 140.2459 | 0.0087
986 | 142.80 | 142.3051 | 0.0035 | 144.10 | 141.4349 | 0.0185 | 142.00 | 141.3055 | 0.0049 | 141.12 | 139.3921 | 0.0122
985 | 143.40 | 142.0016 | 0.0098 | 142.80 | 141.2233 | 0.0110 | 144.10 | 140.3575 | 0.0260 | 142.00 | 139.5268 | 0.0174
984 | 142.21 | 141.8180 | 0.0028 | 143.40 | 140.7662 | 0.0184 | 142.80 | 140.4219 | 0.0167 | 144.10 | 146.7834 | 0.0186
983 | 142.03 | 142.0947 | 0.0005 | 142.21 | 140.1053 | 0.0148 | 143.40 | 140.8399 | 0.0179 | 142.80 | 141.7092 | 0.0076
982 | 142.59 | 140.8774 | 0.0120 | 142.03 | 141.0675 | 0.0068 | 142.21 | 142.7018 | 0.0035 | 143.40 | 137.5108 | 0.0411
981 | 141.18 | 141.3493 | 0.0012 | 142.59 | 142.9580 | 0.0026 | 142.03 | 138.6157 | 0.0240 | 142.21 | 135.7893 | 0.0451
980 | 141.42 | 143.1335 | 0.0121 | 141.18 | 139.4681 | 0.0121 | 142.59 | 136.6854 | 0.0414 | 142.03 | 135.3345 | 0.0471
979 | 142.23 | 141.9348 | 0.0021 | 141.42 | 136.7925 | 0.0327 | 141.18 | 135.8861 | 0.0375 | 142.59 | 135.7501 | 0.0480
978 | 145.33 | 136.8286 | 0.0585 | 142.23 | 135.5206 | 0.0472 | 141.42 | 135.6964 | 0.0405 | 141.18 | 135.9612 | 0.0370
977 | 138.41 | 135.1052 | 0.0239 | 145.33 | 135.1948 | 0.0697 | 142.23 | 135.5496 | 0.0470 | 141.42 | 136.0220 | 0.0382
976 | 135.57 | 135.1128 | 0.0034 | 138.41 | 135.2206 | 0.0230 | 145.33 | 135.5166 | 0.0675 | 142.23 | 134.9895 | 0.0509
975 | 134.84 | 135.5939 | 0.0056 | 135.57 | 135.6036 | 0.0002 | 138.41 | 134.6630 | 0.0271 | 145.33 | 134.4826 | 0.0746
974 | 135.62 | 135.9134 | 0.0022 | 134.84 | 134.9537 | 0.0008 | 135.57 | 134.4806 | 0.0080 | 138.41 | 135.1341 | 0.0237
973 | 135.84 | 136.2169 | 0.0028 | 135.62 | 134.0258 | 0.0118 | 134.84 | 135.2289 | 0.0029 | 135.57 | 135.5020 | 0.0005
972 | 136.30 | 135.5374 | 0.0056 | 135.84 | 134.5033 | 0.0098 | 135.62 | 135.6042 | 0.0001 | 134.84 | 134.6588 | 0.0013
971 | 135.02 | 136.0409 | 0.0076 | 136.30 | 134.9620 | 0.0098 | 135.84 | 134.7869 | 0.0078 | 135.62 | 136.5069 | 0.0065
970 | 134.83 | 136.8923 | 0.0153 | 135.02 | 136.8753 | 0.0137 | 136.30 | 136.7761 | 0.0035 | 135.84 | 138.5646 | 0.0201
969 | 136.09 | 136.5405 | 0.0033 | 134.83 | 136.2487 | 0.0105 | 135.02 | 130.9540 | 0.0301 | 136.30 | 143.5801 | 0.0534
968 | 136.71 | 135.4534 | 0.0092 | 136.09 | 137.1882 | 0.0081 | 134.83 | 134.5044 | 0.0024 | 135.02 | 138.9483 | 0.0291
967 | 135.34 | 136.0370 | 0.0051 | 136.71 | 135.3722 | 0.0098 | 136.09 | 136.8509 | 0.0056 | 134.83 | 137.4119 | 0.0191
966 | 134.48 | 135.5328 | 0.0078 | 135.34 | 136.1922 | 0.0063 | 136.71 | 136.7664 | 0.0004 | 136.09 | 138.0672 | 0.0145
965 | 136.64 | 134.7746 | 0.0137 | 134.48 | 135.9564 | 0.0110 | 135.34 | 137.3660 | 0.0150 | 136.71 | 137.6435 | 0.0068
964 | 133.41 | 135.5599 | 0.0161 | 136.64 | 136.7964 | 0.0011 | 134.48 | 137.1349 | 0.0197 | 135.34 | 137.0746 | 0.0128
963 | 135.11 | 136.0755 | 0.0071 | 133.41 | 136.6319 | 0.0242 | 136.64 | 136.5669 | 0.0005 | 134.48 | 137.0378 | 0.0190
962 | 135.11 | 136.3818 | 0.0094 | 135.11 | 136.1917 | 0.0080 | 133.41 | 136.5466 | 0.0235 | 136.64 | 138.1209 | 0.0108
961 | 136.25 | 135.7434 | 0.0037 | 135.11 | 136.0128 | 0.0067 | 135.11 | 137.5760 | 0.0183 | 133.41 | 126.0336 | 0.0553
960 | 135.78 | 135.3767 | 0.0030 | 136.25 | 136.9301 | 0.0050 | 135.11 | 134.5556 | 0.0041 | 135.11 | 128.1953 | 0.0512
959 | 135.01 | 136.2514 | 0.0092 | 135.78 | 134.3601 | 0.0105 | 136.25 | 127.2257 | 0.0662 | 135.11 | 141.2927 | 0.0458
958 | 135.11 | 134.9542 | 0.0012 | 135.01 | 134.4525 | 0.0041 | 135.78 | 132.8486 | 0.0216 | 136.25 | 139.1490 | 0.0213
957 | 136.84 | 133.1440 | 0.0270 | 135.11 | 130.0163 | 0.0377 | 135.01 | 132.2245 | 0.0206 | 135.78 | 145.3409 | 0.0704
956 | 132.39 | 130.5360 | 0.0140 | 136.84 | 131.4004 | 0.0398 | 135.11 | 134.8190 | 0.0022 | 135.01 | 137.9683 | 0.0219
955 | 133.21 | 129.2345 | 0.0298 | 132.39 | 133.5173 | 0.0085 | 136.84 | 137.1225 | 0.0021 | 135.11 | 137.3024 | 0.0162
954 | 127.02 | 132.9641 | 0.0468 | 133.21 | 118.8850 | 0.1075 | 132.39 | 154.8653 | 0.1698 | 136.84 | 140.8124 | 0.0290
953 | 130.66 | 136.5636 | 0.0452 | 127.02 | 137.1733 | 0.0799 | 133.21 | 102.4452 | 0.2309 | 132.39 | 118.3449 | 0.1061
(98 rows)  ``````

You can use more industry data to verify the feasibility of this prediction technique.

It could be the sales data of the catering and retail industries, the traffic data of people and vehicles of shopping malls, the traffic data of people of railway stations and bus stations, and production and sales data of the agricultural and sideline industry.

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digoal

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digoal

281 posts | 24 followers

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