VerticaPy

Python API for Vertica Data Science at Scale

Example: Methods in a Time Series Model

In this example, we use the 'commodities' dataset to demonstrate the methods available to time series models.

In [1]:
from verticapy.datasets import load_commodities
commodities = load_commodities()
display(commodities)
📅
date
Date
123
Gold
Float
123
Oil
Float
123
Spread
Float
123
Vix
Float
123
Dol_Eur
Float
123
SP500
Float
11986-01-01345.56136363636422.92545454545451.0514285714285718.12136363636361.12159999999858211.779999
21986-02-01339.052515.45473684210530.73684210526315820.62421052631581.07880000000296226.919998
31986-03-01346.09473684210512.61250.56423.5641.04850000000442238.899994
41986-04-01340.71590909090912.84363636363640.60409090909090923.01545454545451.05259999999544235.520004
51986-05-01342.32515.3776190476190.64238095238095218.88751.03720000000612247.350006
61986-06-01342.79761904761913.42571428571430.61476190476190518.59809523809521.0399999999936250.839996
71986-07-01348.55434782608711.58454545454550.63681818181818219.63909090909091.01029999999446236.119995
81986-08-01376.2915.09666666666670.8395238095238118.63809523809520.979300000000876252.929993
91986-09-01418.15227272727314.86666666666671.1014285714285722.70523809523810.973200000000361231.320007
101986-10-01423.86304347826114.89681818181821.1472727272727322.52391304347830.961600000000544243.979996
111986-11-01396.982515.22157894736840.97388888888888918.63157894736840.967399999999543249.220001
121986-12-01391.59523809523816.1076190476190.84090909090909119.75863636363640.957399999999325242.169998
131987-01-01408.5238095238118.65142857142860.858520.76666666666670.900400000000445274.079987
141987-02-01401.04517.74894736842110.85263157894736823.44631578947370.882600000000821284.200012
151987-03-01408.84772727272718.30285714285710.82409090909090921.83727272727270.883599999999206291.700012
161987-04-01439.66518.67714285714290.99857142857142926.88142857142860.872100000000501288.359985
171987-05-01461.6519.43750.851525.41150.861100000000079290.100006
181987-06-01449.27727272727320.07318181818180.82909090909090921.62454545454550.876500000000306304.0
191987-07-01450.33043478260921.34217391304351.0045454545454517.80090909090910.888600000000224318.660004
201987-08-01460.987520.31095238095241.0076190476190520.84904761904760.89600000000064329.799988
211987-09-01460.12045454545519.531.0780952380952422.89380952380950.873499999999694321.829987
221987-10-01465.76363636363619.85909090909091.1223809523809558.21954545454550.868599999999788251.789993
231987-11-01468.14047619047618.8541.1678947368421149.43650.814700000000812230.300003
241987-12-01487.07857142857117.27454545454551.1341.76409090909090.791400000000067247.080002
251988-01-01477.757517.12951.0357894736842138.33650.800100000000384257.070007
261988-02-01442.1238095238116.79571428571431.031533.6740.822000000000116267.820007
271988-03-01443.49130434782616.19739130434781.1030434782608729.35695652173910.810900000000402258.890015
281988-04-01451.55789473684217.86251.12927.4050.805700000000798261.329987
291988-05-01451.3217.42363636363641.0933333333333325.71666666666670.813099999999395262.160004
301988-06-01451.65681818181816.52772727272730.89545454545454625.27090909090910.842500000000655273.5
311988-07-01437.45238095238115.49750.77723.6440.886300000000119272.019989
321988-08-01431.06363636363615.52347826086960.63173913043478323.70739130434780.905899999999747261.519989
331988-09-01413.43863636363614.53545454545450.51380952380952419.52428571428570.900299999999334271.910004
341988-10-01406.39047619047613.77047619047620.441520.48190476190480.87960000000021278.970001
351988-11-01419.96590909090914.14136363636360.296521.65904761904760.844499999999243273.700012
361988-12-01419.247516.3823809523810.017142857142857117.75428571428570.843999999999141277.720001
371989-01-01404.44523809523818.0242857142857-0.08417.75809523809520.880499999999302297.470001
381989-02-01387.972517.9365-0.20263157894736818.31263157894740.888899999999921288.859985
391989-03-01390.2738095238119.4840909090909-0.32181818181818217.47545454545450.896699999999328294.869995
401989-04-01384.7221.069-0.27616.88750.899199999999837309.640015
411989-05-01371.3520.1234782608696-0.15863636363636417.4350.9375320.519989
421989-06-01367.72727272727320.0504545454545-0.12909090909090916.73227272727270.955400000000736317.980011
431989-07-01375.20952380952419.78142857142860.19918.05650.914000000000669346.079987
441989-08-01365.54772727272718.5778260869565-0.032173913043478319.32347826086960.927799999999479351.450012
451989-09-01361.79761904761919.5914285714286-0.09416.74650.941100000000006349.149994
461989-10-01366.820.09772727272730.028571428571428621.95090909090910.907199999999648340.359985
471989-11-01394.36136363636419.85590909090910.078571428571428620.9323809523810.893899999999121345.98999
481989-12-01409.65526315789521.10.05918.2350.856499999999869353.399994
491990-01-01410.11818181818222.86318181818180.12142857142857123.49909090909090.831899999999223329.079987
501990-02-01416.542522.1130.10263157894736823.32684210526320.820799999999508331.890015
511990-03-01393.66136363636420.3877272727273-0.038181818181818218.83136363636360.835100000000239339.940002
521990-04-01374.92894736842118.42550.061520.45750.82510000000002330.799988
531990-05-01368.85476190476218.19954545454550.11590909090909118.15181818181820.811400000000504361.230011
541990-06-01352.65714285714316.69523809523810.12904761904761917.58285714285710.817699999999604358.019989
551990-07-01361.82045454545518.45409090909090.31428571428571418.93285714285710.792699999999968356.149994
561990-08-01394.86136363636427.30739130434780.69130434782608727.77521739130430.759899999999106322.559998
571990-09-01389.5633.50750.81368421052631628.81894736842110.761699999999109306.049988
581990-10-01381.33260869565236.03956521739130.84181818181818230.13043478260870.739799999999377304.0
591990-11-01381.86590909090932.33227272727270.793525.10666666666670.72400000000016322.220001
601990-12-01378.16052631578927.2810.76122.58450.731999999999971330.220001
611991-01-01384.59090909090925.23409090909090.96761904761904826.93181818181820.736699999999473343.929993
621991-02-01363.747520.47750.98789473684210522.11263157894740.722599999999147367.070007
631991-03-01363.3919.90151.00719.04105263157890.781800000000658375.220001
641991-04-01358.05476190476220.831.0909090909090918.92727272727270.826699999999619375.339996
651991-05-01357.11666666666721.23227272727271.2840909090909117.18045454545450.83389999999963389.829987
661991-06-01366.3620.1891.32817.5750.868700000000899371.160004
671991-07-01368.01304347826121.40304347826091.35517.66954545454550.870699999999488387.809998
681991-08-01356.72142857142921.69363636363641.4668181818181815.93272727272730.850300000000061395.429993
691991-09-01348.45952380952421.88651.468517.0290.828100000000632387.859985
701991-10-01358.82608695652223.23086956521741.61516.84636363636360.82550000000083392.450012
711991-11-01359.95952380952422.46095238095241.8584210526315817.68150.795000000000073375.220001
721991-12-01361.87519.49809523809522.0628571428571418.01857142857140.768099999999322417.089996
731992-01-01354.43636363636418.78545454545452.0742857142857117.49954545454550.770599999999831408.779999
741992-02-01353.852519.01252.1257894736842117.05052631578950.791999999999462412.700012
751992-03-01344.64090909090918.92181818181821.8572727272727316.22272727272730.812700000000404403.690002
761992-04-01338.727520.232.1361904761904816.18857142857140.804899999999179414.950012
771992-05-01337.03947368421120.97552.166514.7280.78859999999986415.350006
781992-06-01340.78409090909122.38454545454552.2136363636363614.75318181818180.767900000000736408.140015
791992-07-01352.45217391304321.77521739130432.4890909090909113.30363636363640.729799999999159424.209991
801992-08-01343.602521.33904761904762.3928571428571414.4223809523810.71349999999984414.029999
811992-09-01345.321.88136363636362.5209523809523814.25857142857140.721999999999753417.799988
821992-10-01344.27727272727321.68590909090912.5061904761904817.47863636363640.755199999999604418.679993
831992-11-01334.92380952380920.33852.2936842105263214.5940.807199999999284431.350006
841992-12-01334.65714285714319.41409090909092.0963636363636412.78136363636360.807199999999284435.709991
851993-01-01328.992519.0322.2112.5840.825000000000728438.779999
861993-02-01329.3120.08611111111112.1605263157894713.62473684210530.845300000000861443.380005
871993-03-01329.97391304347820.32217391304352.0256521739130413.69304347826090.848400000000766451.670013
881993-04-01341.947520.25252.1319047619047613.45666666666670.820100000000821440.190002
891993-05-01367.04473684210519.94952.058513.48550.821200000000317450.190002
901993-06-01371.91363636363619.09409090909091.7990909090909112.97318181818180.844300000000658450.529999
911993-07-01392.03409090909117.891.7333333333333311.86950.878399999999601448.130005
921993-08-01379.79523809523818.01090909090911.6763636363636411.95318181818180.882400000000416463.559998
931993-09-01355.56136363636417.50428571428571.5128571428571412.6490.848200000000361458.929993
941993-10-01364.00476190476218.15333333333331.459511.37523809523810.859300000000076467.829987
951993-11-01373.93863636363616.60904761904761.568513.37095238095240.885800000000018461.790009
961993-12-01383.24285714285714.51476190476191.5618181818181810.86454545454550.885800000000018466.450012
971994-01-01387.1115.02666666666671.610510.60523809523810.897499999999127481.609985
981994-02-01381.657514.78105263157891.4989473684210512.88526315789470.894800000000032467.140015
991994-03-01384.014.68086956521741.4873913043478314.24086956521740.876000000000204445.769989
1001994-04-01377.90789473684216.421.4226315789473715.33473684210530.877300000000105450.910004
Rows: 1-100 | Columns: 7

Let's create a VAR model to predict the change in our variables over time.

In [3]:
from verticapy.learn.tsa import VAR
model = VAR("VAR_commodities", p = 15)
model.drop()
model.fit(input_relation = "commodities",
          X = ["Gold", "Oil", "Spread", "Vix", "Dol_Eur", "SP500"],
          ts = "date")

Fitting the model creates new model attributes.

In [4]:
model.X
Out[4]:
['"Gold"', '"Oil"', '"Spread"', '"Vix"', '"Dol_Eur"', '"SP500"']
In [5]:
model.ts
Out[5]:
'"date"'
In [6]:
model.input_relation
Out[6]:
'commodities'
In [7]:
model.test_relation
Out[7]:
'commodities'

Since we didn't write a test relation when fitting the model, the model will use the training relation as the test relation. After fitting the model, the These attributes will be used when invoking the different model abstractions. For example, let's compute the R2 of the model.

In [8]:
model.score(method = "r2")
Out[8]:
r2
"Gold"0.9969178988297
"Oil"0.984505390437316
"Spread"0.98333383705978
"Vix"0.809280825408277
"Dol_Eur"0.976525112259812
"SP500"0.995213614862105
Rows: 1-6 | Columns: 2

The 'score' method uses the 'y' attribute and the model prediction in the 'test_relation' to compute the accuracy of the model. You can change these attributes at any time to deploy the models on different columns.

Models have many useful attributes. For example, the 'coef_' attribute gives us the p-value of the model.

In [9]:
for elem in model.coef_:
    display(elem)
Abc
predictor
Varchar(65000)
123
coefficient
Float
123
std_err
Float
123
t_value
Float
123
p_value
Float
1Intercept28.536246891519123.45761671585351.216502393963720.224719007474869
2ar0_11.212495463112090.058300326851587720.79740421007739.38585656197178e-61
3ar0_2-0.2657172763833840.0911043814875882-2.916624557948230.00379668763754037
4ar0_30.1236049240760760.09244348250144171.337086409246310.18217474351721
5ar0_4-0.08465352359058540.0922931959314058-0.9172238834756760.35973835612789
6ar0_50.1475883108740850.09347587727798361.578891957709880.11538065396013
7ar0_6-0.07467377115272230.0964409915451842-0.7742949336821830.439346523264487
8ar0_70.03003985021706510.09494504694901030.3163919675893980.751917949570274
9ar0_8-0.2825957175900340.0934137195443952-3.025205708201450.00269259813755072
10ar0_90.2114659166363810.09457464049068482.235968495774610.0260652425208447
11ar0_10-0.0289682147487440.0955704715244925-0.3031084213215390.762010542572336
12ar0_110.1524238528568420.09477266262891351.608310335794450.108785149004158
13ar0_12-0.1609365867255260.094521878486629-1.702638471666560.089638133407115
14ar0_130.08667056505930760.09481950266658470.9140584228127490.361396529767663
15ar0_140.02827987223163570.09418508373997560.3002585028188780.764181233977993
16ar0_15-0.106574371683990.0616194550652301-1.729557192142830.0847048269578117
17ar1_10.1863309243810110.4465079686132980.4173070526819290.67674268420536
18ar1_2-0.3838352151493040.719190789680871-0.5337042974641330.593928600425446
19ar1_3-1.233452950335280.730929242853722-1.687513480127820.0925104852369963
20ar1_42.172177349679370.7351186813212162.954866207148150.00336774173477664
21ar1_5-0.8972704694919860.751437855340256-1.194071423359020.233362918851882
22ar1_6-0.7221964401502420.75015814215371-0.9627255902026340.33643560184064
23ar1_70.7292592706094180.7363193796618120.9904116213053680.322745378832708
24ar1_80.6531397262352240.733784878425530.8900970099529110.374104035524252
25ar1_9-1.382407119715480.741297102456291-1.864848945361950.0631473354628441
26ar1_101.421271557902510.7433760832690831.911914561001620.0568088195382841
27ar1_11-0.4859731638262020.746682470054503-0.6508431405798630.515629858830073
28ar1_12-0.7995376859789760.747637946531285-1.069418278845370.285713217588517
29ar1_130.6870730341147460.7455300836061860.9215899522006160.357459156835338
30ar1_14-0.7877873503674660.744855541380698-1.057637765448030.291044074790766
31ar1_150.7160574430496350.4725560433692681.515285759429150.130719006743371
32ar2_1-2.7932478764123513.5713219264952-0.2058198819202070.837066706683407
33ar2_212.860637429672421.88327013108650.5876926689947990.55716643483518
34ar2_3-5.6644193120846522.6755409970521-0.249803050468390.802905111952815
35ar2_424.180697465100823.18614932345571.042894062647950.297809959015635
36ar2_5-44.53895138947723.6750258753874-1.881263050097860.0608731390576656
37ar2_617.201770091087423.90236728391570.7196680515683780.472271605925185
38ar2_718.333591040283923.81724502985770.7697611968680920.442027712401021
39ar2_8-10.334662689334423.5469658756951-0.4388957262643230.661042831989565
40ar2_9-18.930740950043923.4188393201874-0.8083552174050420.419506019701915
41ar2_107.1386040815850723.40278862908090.3050321991420490.76054632453699
42ar2_118.8682682917860823.24458586626820.3815197372328950.703078993345408
43ar2_12-2.9038816524844223.0213247115647-0.1261387730231550.89970384535311
44ar2_13-5.2941515294082822.5976584117239-0.2342787660982440.814923221458952
45ar2_14-3.2404902134367121.6450388143914-0.1497105291066590.88109035429166
46ar2_158.3027573591061813.41958084900610.6187046713699050.536565284150822
47ar3_1-0.5027462314382090.510054281970306-0.9856720141553050.325062760565841
48ar3_21.12411961964040.6280470440443211.78986531391280.0744514757095814
49ar3_30.06391420005229520.6292719945663250.1015684800915750.919164861770739
50ar3_4-0.805497838790870.620317126196823-1.298525874546450.195071822840087
51ar3_50.6091150609886430.6242358391386760.975777138699860.329935733930905
52ar3_6-0.04453454311715180.656703707290886-0.06781527593451710.94597640165073
53ar3_7-1.174309257422690.683845863352519-1.717213366862680.0869389378836803
54ar3_80.7024618181908560.6818779363461291.030187047780170.303725291101653
55ar3_9-0.8503067836692250.688606542738108-1.23482239986880.217831730394892
56ar3_101.242028899205760.6985074602923091.778118301966310.076364539869516
57ar3_11-0.2810453517158180.70794423948742-0.3969879773572370.691649495689552
58ar3_120.2439018755034110.7071052295158240.3449300971376190.730380829654549
59ar3_131.088345939898770.711864680652131.528866327378050.127317295238838
60ar3_14-0.8170926826928730.688290205843594-1.187133967265180.236083662488294
61ar3_15-0.317155618971810.502041192339552-0.6317322638284720.528027414824921
62ar4_1-46.608095483796295.4243667945713-0.4884297066820860.625590823194623
63ar4_2-92.9647046344762153.445373485837-0.6058488602333560.545058446285002
64ar4_3234.503308695101159.145376352351.47351631615050.141626537557194
65ar4_4-140.101600778959159.318926408608-0.879378263067360.379877356433741
66ar4_532.352148242775159.4637317923710.2028809176803850.839361203541782
67ar4_6-15.6352645152074160.642067356053-0.09732982631848730.922527366752222
68ar4_7112.90352543657161.2823329396930.7000365345582320.484429490600711
69ar4_8-172.016837197269160.406214041056-1.072382626980030.284382315124078
70ar4_982.7850488694094161.3999277571030.5129187479810770.608373459980389
71ar4_10-124.571371472994160.584913041091-0.7757352114461560.438496730959977
72ar4_1181.6149826794426160.0790596517880.5098417173175290.610525132819986
73ar4_1288.0632092895976160.8982113679150.5473224875584830.584551003184447
74ar4_13-199.332941775472160.146804337156-1.244688850336450.214186303391015
75ar4_14358.387652307923154.1002128247172.325679152147410.0206805893591999
76ar4_15-242.53992666257591.833173804137-2.641092718627670.00868270462061203
77ar5_1-0.06438235093784430.0375621688308582-1.714021126622290.087524415311534
78ar5_20.09537027877085080.04848754643294171.966902551003440.0500860818582451
79ar5_30.08893043290304090.04985671819359551.783720150967830.0754472822621015
80ar5_4-0.07727055770613590.0498135645032031-1.551195110744730.121875209388849
81ar5_5-0.07429311124477070.050296861260262-1.477092394699140.14066602430672
82ar5_60.04498200516937350.05292886014082780.8498578100811140.39606001252256
83ar5_7-0.04690586717959630.0530494310654534-0.8841917102131220.377277965115729
84ar5_8-0.04512938692550250.052816929499234-0.854449271348820.393515924634555
85ar5_90.01642055844626710.05204980922706130.3154777834945430.752611187849938
86ar5_100.1015015644999830.05229986801992821.940761388944760.0531935246033809
87ar5_11-0.01711949714616880.0522072523665041-0.3279141569448410.743197926015103
88ar5_120.01577646920815570.05244384781240790.3008259284213520.763748895009399
89ar5_130.03625698244010780.05272836615243270.6876181661933590.492207405051925
90ar5_140.01513946647662580.05277874448593720.2868477949614670.774420491492481
91ar5_15-0.07730205588347870.0426604647957568-1.812030324882150.0709489757714002
Rows: 1-91 | Columns: 5
Abc
predictor
Varchar(65000)
123
coefficient
Float
123
std_err
Float
123
t_value
Float
123
p_value
Float
1Intercept-0.003312827053605363.20498795635841-0.00103364727066540.999175933785173
2ar0_10.007272143575888460.00796550850303630.9129540911438930.361976146683964
3ar0_2-0.01275801034824920.0124474898271258-1.024946437027550.306187577961975
4ar0_30.02237247520105610.01263044970210951.771312639590310.0774911865400367
5ar0_40.005636245021345850.01260991621600120.4469692680585620.655209359904166
6ar0_5-0.02245536498673940.012771504646655-1.75823958163150.0796935374323065
7ar0_60.006882045767881740.01317662489525980.5222919998547960.601840057485922
8ar0_7-0.004671453260382210.0129722356569074-0.3601116556878750.719008857075765
9ar0_8-0.008462359800899930.0127630121049804-0.6630378261255220.507799032765273
10ar0_9-0.001407847661806130.0129216274364615-0.108952813314640.913310406863298
11ar0_10-0.0005919017415867420.0130576867176998-0.04532975513835960.963873656391934
12ar0_110.01733286484210960.01294868297990411.338581295797390.181687870589536
13ar0_12-0.01717115857800610.0129144186228126-1.329611427313820.184623881195283
14ar0_130.005170984476465360.01295508268190410.3991471612673130.690059600334008
15ar0_140.000119673282942490.01286840273296910.009299777557931460.992585939849527
16ar0_150.002914003940854390.008418997281508150.3461224470584920.729485485412894
17ar1_11.305046501643040.061005884747725521.39214121784695.37470853731338e-63
18ar1_2-0.2987529681969720.0982622338480484-3.040364100199060.00256445086731384
19ar1_3-0.09447111773852870.099866045586512-0.9459783571453250.344896380826558
20ar1_40.06619912520982750.1004384438823350.6591014620594950.510319898222507
21ar1_5-0.03684711505161790.102668114390737-0.3588954104229880.719917642984825
22ar1_6-0.008448944626650250.102493268608228-0.08243414168930070.934354687631606
23ar1_7-0.0008208927465602760.100602493954746-0.008159765372511910.993494769929288
24ar1_80.05656951766295040.1002562078833150.564249524865230.572992330624243
25ar1_9-0.03170342732905240.101282594657202-0.3130195018833670.754476329588307
26ar1_100.06064534165106750.1015666434822470.5970990038837670.55087713138308
27ar1_11-0.04003057820187910.102018391413621-0.3923858987305550.695042748561787
28ar1_120.0603260907884220.1021489370968310.590569931542570.555238901629821
29ar1_13-0.1323432549371830.10186094214106-1.299254180801810.194822152956317
30ar1_140.04269559906647660.1017687801906390.4195353328053720.675115523934597
31ar1_150.04669920358976040.06456480409108690.7232919583226490.470045915218135
32ar2_1-2.583653176637991.85423454791482-1.393379914932250.164503151458173
33ar2_23.817541191953752.98988674192421.276817993947460.202622091538284
34ar2_3-0.681391066442873.09813382492293-0.2199359695057140.826065780979161
35ar2_42.069176225354343.167897669918910.6531701591886660.514130715207167
36ar2_5-2.648490280641453.23469232685557-0.8187765676051290.413542869255162
37ar2_6-0.9317941970944053.26575372943286-0.2853228609054430.775587337371381
38ar2_72.581080422784793.254123570990990.7931722217908180.428284133770991
39ar2_8-5.087006662358253.21719563221363-1.581192828755050.114853671333204
40ar2_93.42489845739823.199689844125011.070384513576120.285278943559098
41ar2_10-0.8065558629584763.19749685613689-0.2522460222002940.801018066971676
42ar2_11-2.065776302946693.17588178945645-0.6504575547505640.51587848616302
43ar2_123.403484107655973.145377867399281.082058897575350.280067337184685
44ar2_13-0.3855369864626543.08749281432879-0.1248705696328780.900706952631226
45ar2_14-0.05520041922090972.95733746336432-0.01866558007151230.985119894286093
46ar2_15-0.1296921240098071.83350233407877-0.07073463807449710.943654562153457
47ar3_1-0.0526793224295190.0696881465242403-0.7559294522375490.450265515763487
48ar3_20.03748313874157820.08580936574124210.4368187367170210.662546913752644
49ar3_3-0.02011651031082810.0859767293620956-0.2339762219391520.81515788098011
50ar3_4-0.07238217131239820.0847532357044628-0.8540343116196430.393745440537419
51ar3_50.1675272515487480.08528864507427211.964238632268810.0503955849597349
52ar3_60.02580325690679790.08972469361479330.287582558014370.773858449582504
53ar3_7-0.1113687750393590.0934330960642349-1.191962802589720.23418750780549
54ar3_80.1841358694532060.09316422039664791.976465521519370.0489881830529803
55ar3_9-0.2398210225536630.0940835423682955-2.54902203421370.0112841409772017
56ar3_100.1856601149010830.09543629366875761.945382702575140.0526326906844688
57ar3_11-0.1151733807789060.0967256302639243-1.190722463783850.234673517735206
58ar3_120.0797702686185850.09661099726915740.825685179466120.409617669227945
59ar3_13-0.09005887781435720.0972612757588912-0.9259479388036340.355193308061036
60ar3_14-0.04573985387992250.0940403216119267-0.4863855535147550.627037494168276
61ar3_150.07627900530684430.06859332704317891.112047025490210.266979586354225
62ar4_1-8.9473093793076313.0377245917329-0.6862631064458150.493060165037644
63ar4_27.3947889423034220.96506989342430.3527194986658640.724538437350074
64ar4_3-8.4147827287597521.7438549149238-0.3869959012182470.699024772876226
65ar4_421.722639037390821.7675668651560.9979360197653920.319088694622401
66ar4_5-25.227534432873721.7873514629093-1.157898172057350.247796903117786
67ar4_611.620196573051521.94834613410270.5294338125548520.596883471476571
68ar4_78.3255395637461322.03582490525390.3778183752840190.705824079790489
69ar4_8-30.725496430084121.9161217592572-1.401958647957680.161928084882079
70ar4_926.663639794627722.05189175373611.209131628814130.227533648561226
71ar4_10-16.417369753726221.9405372039235-0.7482665351872260.454866703093321
72ar4_1135.394510208010821.87142302066951.618299375150920.106615048893171
73ar4_12-31.925415567699921.9833428041845-1.452254820937560.147442119945856
74ar4_1320.170556635521321.8806789013220.9218432721620280.357327198344179
75ar4_14-13.54281943605221.0545398604667-0.6432256190732940.520553220367178
76ar4_154.4516015042117912.54706390685940.354792287443290.722986450135791
77ar5_10.009696274750559230.005132077149007231.889347036108940.059778398384304
78ar5_2-0.01699882959686950.00662479928090568-2.565938812042860.0107594772510146
79ar5_30.002770914380411330.006811867688418530.4067774811777990.684452128058598
80ar5_4-0.008346424935787410.00680597164792749-1.226338481490590.221002007160034
81ar5_50.02227488816045660.006872003943726493.241396300534950.00131887541129843
82ar5_6-0.001867506954123850.0072316110093352-0.2582421747675740.796391373792244
83ar5_7-0.00259537264451870.00724808448002035-0.3580770411372760.720529357533611
84ar5_80.002459613694269170.007216318050864580.3408405334870860.733454511803387
85ar5_9-0.008279044727722340.00711150726538822-1.164175809538510.245247944881215
86ar5_100.004955757181619450.007145672518799420.6935326477083070.488494655545902
87ar5_11-0.001084923153815250.00713301854557657-0.1520987428930850.879207996253276
88ar5_12-0.003678964877886550.00716534431693842-0.5134386730292510.60801022789806
89ar5_13-0.00357489323141280.00720421773976686-0.4962222632000140.620089253817064
90ar5_14-0.00020859300637710.00721110087517226-0.02892665211428170.9769417024462
91ar5_150.00398787521617550.005828651628988720.6841848629864680.4943695795084
Rows: 1-91 | Columns: 5
Abc
predictor
Varchar(65000)
123
coefficient
Float
123
std_err
Float
123
t_value
Float
123
p_value
Float
1Intercept-0.08043183988287690.100242229731371-0.8023748084855870.422950861457032
2ar0_10.000105988395660520.0002491367656169940.4254225400977510.670823851912084
3ar0_2-0.0005858106883871310.000389319445757727-1.504704413741720.133418235876387
4ar0_30.0005607519953661770.0003950418715731981.419474834738560.156765382046698
5ar0_40.0003085764579803250.0003943996468723010.7823953708564960.434579471686117
6ar0_5-0.0006606686988079870.000399453639214266-1.65393085442290.0991537432362838
7ar0_60.0001972617510490990.0004121245626568680.4786459457242740.632527936851413
8ar0_71.3500852206666e-050.0004057318918373420.03327530440244140.973476461418362
9ar0_80.0005654333328524930.0003991880184615091.41645867787240.157645310092673
10ar0_9-0.0005829705764201880.000404149021346311-1.442464402061850.150180979571038
11ar0_10-0.0001440225925234680.000408404539904473-0.3526468940750650.724592820236949
12ar0_110.0003551410965170090.0004049952360710480.8769019111491640.381218952705149
13ar0_120.0001789000344283310.0004039235516834840.4429056777766640.658142859893796
14ar0_130.0001698304159006850.0004051953991939160.4191321427601120.675409833223998
15ar0_14-0.0008138737658184990.000402484315260819-2.022125421933740.0440219694537639
16ar0_150.0003482709771269050.0002633205088731861.322612426268060.186939185193719
17ar1_10.0009971439623117220.001908077658050070.5225908694569260.601632261442651
18ar1_2-0.001809837206331540.00307334241296358-0.5888823837843510.556369025158628
19ar1_30.002118780524276160.003123504743344250.6783343386274620.498065763495443
20ar1_4-2.10470818869934e-050.00314140761294908-0.006699888865181340.994658611539072
21ar1_5-0.002202677476581570.00321114489320472-0.6859477070756830.493258764769414
22ar1_60.0007168367402825560.003205676251407480.2236148269706970.823204314192409
23ar1_7-0.0007804947151395240.00314653859792313-0.248048670260930.804260975878767
24ar1_8-3.6045724954803e-050.00313570782776181-0.01149524347762070.990835718867679
25ar1_9-0.00191185073772560.00316781006969912-0.6035244208650370.546601211341372
26ar1_100.002763025202120680.003176694248973060.8697800246321770.385093589114493
27ar1_110.003088382325311170.003190823543851550.967895053696160.333851295374327
28ar1_12-0.001702520070322010.00319490661391142-0.5328857071780480.594494485370403
29ar1_13-0.001785145120628110.00318589901172662-0.5603269639299190.575661142375222
30ar1_14-0.0001475505483794490.00318301646753852-0.04635557179305220.963056694620022
31ar1_150.001868278657414120.002019389780051930.925169908191810.355597160410365
32ar2_11.271530105764080.057994790638500821.92490207766955.39776715568308e-65
33ar2_2-0.4574180260770220.0935145210327989-4.891411740392591.61109991524605e-06
34ar2_30.353570793382030.09690015901629973.648815409297270.000309014221509024
35ar2_4-0.3059207899235080.0990821589090473-3.087546671286490.00220059246934958
36ar2_50.1408342687981590.1011712916723041.392037864400540.16490878086164
37ar2_60.06627779395289220.1021427974299950.6488738865637260.516900297015444
38ar2_7-0.07386195180284720.101779041612433-0.7257088555039480.468564774910838
39ar2_80.04466769324540820.1006240485288920.4439067389798270.657419701917299
40ar2_9-0.02388318553029030.100076520970254-0.2386492385900310.811535263729991
41ar2_10-0.02909739639416390.100007931007141-0.2909508886058860.771283442985183
42ar2_11-0.08996286915013480.0993318777709533-0.9056797391626660.365808758310635
43ar2_120.09695032012335150.09837780830042810.9854897338969240.325152101325999
44ar2_13-0.0587318445059970.0965673407208563-0.6081957323001260.543502999430893
45ar2_140.11005870853020.09249647906090431.189869167427790.235008286822051
46ar2_15-0.09527901283227540.0573463503415347-1.66146602643110.0976307962623087
47ar3_16.42029951969381e-050.002179632275864670.0294558838698920.976519957312155
48ar3_2-0.004029085680316010.00268385475105196-1.501230898854260.134313692898461
49ar3_30.002727315678281440.002689089373696011.014215334365360.31127093467648
50ar3_40.001448338109383720.002650822230738430.5463731564452220.585202471344428
51ar3_50.002011358674245620.002667568199765320.7540045928057510.451418796474799
52ar3_6-0.00381365018129820.00280631424279373-1.358953364218270.175149241984144
53ar3_70.004145618863806710.002922301739575371.418614240844650.157016067728795
54ar3_8-0.004480725655603610.00291389213030181-1.537711574497960.125139187067268
55ar3_90.00279545774546430.002942645712385070.9499810778099190.342861888477791
56ar3_10-0.00520090148056590.00298495563943528-1.742371448290550.082435172104692
57ar3_110.002031533976465270.003025282148281390.6715188458105810.502390146924729
58ar3_120.002103477929593980.003021696778491340.6961247549941780.4868722832595
59ar3_13-0.0009328209862657920.00304203550258168-0.3066436882390540.759320457299259
60ar3_14-0.0008017444247716550.00294129389919635-0.2725822213790730.785355856920147
61ar3_150.001301375385017960.002145389667958080.6065916157117360.54456592680126
62ar4_1-0.02522871174943480.407780185602883-0.06186841008994920.950707493721872
63ar4_20.2695627850148410.6557233228978380.4110922634619160.681288917699377
64ar4_3-0.7031269696984170.68008133862193-1.033886580571650.301995057196506
65ar4_40.4666916703328350.6808229759681290.6854816696942420.493552296582137
66ar4_50.216320552668250.6814417777296750.3174453926041260.751119372367432
67ar4_6-0.04463365747724880.686477199090084-0.06501841217218880.948201248048382
68ar4_7-0.4344287779492790.689213267741124-0.63032561658760.52894592558106
69ar4_80.6733428836611640.685469319113560.9823092950854780.326713508534969
70ar4_9-0.6426359429962640.689715789665868-0.9317402220233190.352195856452096
71ar4_100.2214943436692640.6862329596159750.3227684426484190.747088202622181
72ar4_11-0.05021379561166840.684071279157333-0.07340433247471490.941531709981059
73ar4_120.1184608465023590.6875717879902430.1722886956264120.863322993657085
74ar4_130.9879153380627770.684360774820771.443559266413980.149872769914603
75ar4_14-1.143634736736660.65852167007182-1.736669860252180.0834388419440965
76ar4_150.2414918693952190.3924338187013590.6153696697047240.538762103908763
77ar5_1-0.0001151727140303350.000160515691033808-0.7175168563805840.473595557341566
78ar5_2-4.86758404893289e-050.000207203477979779-0.2349180668390090.814427421448386
79ar5_3-9.0394457670423e-050.00021305440613824-0.4242787525913480.67165681451572
80ar5_40.0001906305662738720.0002128699960083250.8955257661883770.371200923019162
81ar5_50.0003567326251148890.0002149352844447581.659721092497380.0979817860645985
82ar5_6-0.0003567033151313380.000226182694598752-1.577058385320460.115801974721429
83ar5_7-9.11669814708666e-050.000226697934423481-0.4021517959690050.687849451603842
84ar5_80.0001159291817269950.0002257043775887790.5136328456075060.607874599207844
85ar5_90.0002734510689876480.0002224262164914171.229401251799830.219853697050717
86ar5_10-0.0005403347186117290.000223494801218695-2.417661241627710.0161971302740111
87ar5_117.17009755965073e-050.0002230990236032750.3213863262979110.748134222645306
88ar5_122.6267235678154e-060.0002241100749529910.01172068488382950.990655999800992
89ar5_130.0003859979755245490.0002253259168327961.713065150028790.0877003681991129
90ar5_14-0.0004062566511668920.000225541200275347-1.80125250140960.0726347446991492
91ar5_150.0002211727234582290.0001823024122315541.213218852953540.225969775696306
Rows: 1-91 | Columns: 5
Abc
predictor
Varchar(65000)
123
coefficient
Float
123
std_err
Float
123
t_value
Float
123
p_value
Float
1Intercept-1.452711378309463.40700076849687-0.4263900941091890.670119548423146
2ar0_10.006928281203756770.008467580521628070.8182126152872610.413864269511716
3