SAP HANA Predictive Analysis Library (PAL)

Recommendations

Info

PAL_BROWNSMOOTH_RESULT_TBL: PAL_BROWNSMOOTH_STATISTIC_TBL: Related Information Forecast Accuracy Measures [page 365] 360 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
3.5.4 Croston's Method The Croston’s method is a forecast strategy for products with intermittent demand. The Croston’s method consists of two steps. First, separate exponential smoothing estimates are made of the average size of a demand. Second, the average interval between demands is calculated. This is then used in a form of the constant model to predict the future demand. Initialization The system checks the first time bucket of the historical values. If it finds a value (not zero), it is set as the Z’s initial value and X is set to 1. Otherwise, Z is set to 1 and X to 2. V(t) = Historical value P(t) = Forecasted value q = Interval between last two periods with demand α = Smoothing factor for the estimates Z = Estimate of demand volume X = Estimate of intervals between demand If 1st value ≠ 0 Z(0) = V(1), X(0) = 1 If 1st value = 0 Z(0) = 1, X(0) = 2 Calculation of Forecast Parameters The forecast is made using a modified constant model. The forecast parameters P and X are determined as follows: If V(t) = 0 q = q + 1 Else Z(t) = Z(t−1) + α[V(t) − Z(t−1)] X(t) = X(t−1) + α[q − X(t−1)] Endif In the last iteration, the parameters Z(f) and X(f) will be delivered for the forecast. Prerequisites ● ● No missing or null data in the inputs. The data is numeric, not categorical. CROSTON This is a Croston's method function. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 361
Page 1 and 2:
SAP HANA Platform SPS 12 Document V
Page 3 and 4:
Naive Bayes. ......................
Page 5 and 6:
1 What is PAL? SAP HANA’s SQLScri
Page 7 and 8:
2 Getting Started with PAL This sec
Page 9 and 10:
The result will tell you whether th
Page 11 and 12:
● : The name of the schema where
Page 13 and 14:
CREATE COLUMN TABLE PAL_ARIMAX_DATA
Page 15 and 16:
For more information on how to use
Page 17 and 18:
Category PAL Algorithm Built-in Fun
Page 19 and 20:
Category PAL Algorithm Built-in Fun
Page 21 and 22:
The procedure name is the same as s
Page 23 and 24:
● USER1 has been assigned the AFL
Page 25 and 26:
3.1.2 Agglomerate Hierarchical Clus
Page 27 and 28:
Table 13: Name Data Type Default Va
Page 29 and 30:
Name Data Type Default Value Descri
Page 31 and 32:
INSERT INTO DATA_TBL VALUES ('17',
Page 33 and 34:
Prerequisites ● ● The input dat
Page 35 and 36:
Page 37 and 38:
INSERT INTO PAL_AD_DATA_TBL VALUES
Page 39 and 40:
The signature table should contain
Page 41 and 42:
CREATE TYPE PAL_KMEANS_CENTERS_T AS
Page 43 and 44:
PAL_CLUSTER_ASSIGNED_TBL: For DBSCA
Page 45 and 46:
For SOM: SET SCHEMA DM_PAL; DROP TY
Page 47 and 48:
PAL_CLUSTER_ASSIGNED_TBL: Related I
Page 49 and 50:
Table 24: Name Data Type Descriptio
Page 51 and 52:
DROP TYPE PAL_CONTROL_T; CREATE TYP
Page 53 and 54:
3.1.6 Gaussian Mixture Model (GMM)
Page 55 and 56:
Table 29: Name Data Type Descriptio
Page 57 and 58:
Output Tables Output format 0: Tabl
Page 59 and 60:
INSERT INTO PAL_GMM_PDATA_TBL VALUE
Page 61 and 62:
Example 2 SET SCHEMA DM_PAL; DROP T
Page 63 and 64:
PAL_GMM_RESULTSMODEL_TBL: 3.1.7 K-M
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Table 43: Table Column Column Data
Page 71 and 72:
INSERT INTO #PAL_CONTROL_TBL VALUES
Page 73 and 74:
PAL_KMEANS_MODEL_TBL: VALIDATEKMEAN
Page 75 and 76:
"V000" DOUBLE, "A0" INTEGER, "A1" I
Page 77 and 78:
Expected Result PAL_SILHOUETTE_RESU
Page 79 and 80:
Table Column Column Data Type Descr
Page 81 and 82:
Example Assume that: ● ● ● DM
Page 83 and 84:
PAL_KMEDIANS_ASSIGN_TBL: 3.1.9 K-Me
Page 85 and 86:
Position Schema Name Table Type Nam
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
The input, parameter, and output ta
Page 93 and 94:
Page 95 and 96:
INSERT INTO PARAMETERS_TBL VALUES (
Page 97 and 98:
DOCTOPICDIST_TBL: GENERALINFO_TBL:
Page 99 and 100:
Procedure Calling CALL .(, , ) with
Page 101 and 102:
Page 103 and 104:
Expected Result DOCTOPICDIST_TBL: 3
Page 105 and 106:
The following parameters are option
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
CREATE TYPE PAL_SOM_MODEL_T AS TABL
Page 113 and 114:
PAL_SOM_RESASSIGN_TBL: PAL_SOM_MODE
Page 115 and 116:
Page 117 and 118:
INSERT INTO PAL_SLIGHT_SIL_PDATA_TB
Page 119 and 120:
AUC Procedure Generation CALL SYS.A
Page 121 and 122:
Table 85: Name Data Type Default Va
Page 123 and 124:
Example 2 SET SCHEMA DM_PAL; DROP T
Page 125 and 126:
3.2.2 Back Propagation Neural Netwo
Page 127 and 128:
CREATEBPNN This function trains a B
Page 129 and 130:
Page 131 and 132:
INSERT INTO PAL_TRAIN_NN_DATA_TBL V
Page 133 and 134:
PAL_TRAIN_NN_RESULT_TBL: PAL_REGRES
Page 135 and 136:
Classification example: SET SCHEMA
Page 137 and 138:
"NAME" VARCHAR(100), "MODEL" CLOB )
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
CREATE TYPE PAL_C45_DATA_T AS TABLE
Page 145 and 146:
CART This function is used for clas
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Prerequisites ● ● The target co
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
Page 159 and 160:
Page 161 and 162:
INSERT INTO PAL_CM_DATA_TBL VALUES(
Page 163 and 164:
Page 165 and 166:
PAL_KNN_RESULTS_TBL: 3.2.8 Logistic
Page 167 and 168:
Procedure Calling CALL .(, , , , )
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
PAL_LOGISTICR_STAT_TBL: PAL_LOGISTI
Page 175 and 176:
Procedure Generation CALL SYS.AFLLA
Page 177 and 178:
Assume that: ● ● ● DM_PAL is
Page 179 and 180:
PAL_FLOGISTICR_FITTED_TBL: Related
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Expected Result PAL_LRMC_MODEL_TBL:
Page 187 and 188:
Table 137: Name Data Type Default V
Page 189 and 190:
SAP HANA Predictive Analysis Librar
Page 191 and 192:
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_
Page 193 and 194:
3.2.10 Naive Bayes Naive Bayes is a
Page 195 and 196:
Page 197 and 198:
● ● USER1 has been assigned the
Page 199 and 200:
Page 201 and 202:
"DOUBLEARGS" DOUBLE, "STRINGARGS" V
Page 203 and 204:
Position Schema Name Table Type Nam
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
DROP TABLE CV_LR_FUNC_TBL; CREATE C
Page 211 and 212:
CREATE COLUMN TABLE FUNC_TBL LIKE F
Page 213 and 214:
DROP TABLE EVALUATION_RESULT_TBL; C
Page 215 and 216:
INSERT INTO PAL_PARA_TBL VALUES('TR
Page 217 and 218:
Page 219 and 220:
3.2.13 Random Forest The ransom for
Page 221 and 222:
Page 223 and 224:
INSERT INTO PAL_RF_DATA_TBL VALUES
Page 225 and 226:
Page 227 and 228:
3.2.14 Support Vector Machine Suppo
Page 229 and 230:
Table 168: Name Data Type Descripti
Page 231 and 232:
Page 233 and 234:
CREATE TABLE PAL_SVM_PDATA_TBL("POS
Page 235 and 236:
Page 237 and 238:
STRINGARGS VARCHAR(100) ); DROP TYP
Page 239 and 240:
Example 3: Support vector classific
Page 241 and 242:
Related Information SAP HANA Smart
Page 243 and 244:
Page 245 and 246:
INSERT INTO PAL_GR_PDATA_TBL values
Page 247 and 248:
Procedure Calling CALL .(, , , ) wi
Page 249 and 250:
3.3.2 Bi-Variate Natural Logarithmi
Page 251 and 252:
Page 253 and 254:
CREATE LOCAL TEMPORARY COLUMN TABLE
Page 255 and 256:
Page 257 and 258:
Where β0…βn are parameters that
Page 259 and 260:
Page 261 and 262:
CREATE LOCAL TEMPORARY COLUMN TABLE
Page 263 and 264:
Page 265 and 266:
Assume we have m observation pairs
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Example 1: Fitting multiple linear
Page 273 and 274:
CREATE COLUMN TABLE PAL_ENET_MLR_PD
Page 275 and 276:
Page 277 and 278:
3.3.5 Polynomial Regression Polynom
Page 279 and 280:
Page 281 and 282:
insert into PAL_PR_PDATA_TBL values
Page 283 and 284:
The input, parameter, and output ta
Page 285 and 286:
3.4 Association Algorithms This sec
Page 287 and 288:
Page 289 and 290:
Page 291 and 292:
"STRINGARGS" VARCHAR (100) ); INSER
Page 293 and 294:
Optional Parameters The following p
Page 295 and 296:
Page 297 and 298:
INSERT INTO PAL_APRIORI_DATA_TBL VA
Page 299 and 300:
PAL_APRIORI_CONS_ITEMS_TBL: LITEAPR
Page 301 and 302:
Page 303 and 304:
INSERT INTO PAL_LITEAPRIORI_DATA_TB
Page 305 and 306:
Page 307 and 308:
Page 309 and 310: FPGROWTH (Relational Output) FP-Gro
Page 311 and 312: Name Data Type Default Value Descri
Page 313 and 314: ); DROP TYPE PAL_FPGROWTH_POSTRULE_
Page 315 and 316: PAL_FPGROWTH_VALUES_TBL: 3.4.3 K-Op
Page 319 and 320: INSERT INTO PAL_KORD_DATA_TBL VALUE
Page 321 and 322: Prerequisite No missing or null dat
Page 323 and 324: . Initial P 0 can be calculated by
Page 325 and 326: Output Table (Model Table) Table 24
Page 327 and 328: DROP TYPE PAL_ARIMAX_DATA_T; CREATE
Page 329 and 330: CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
Page 331 and 332: We get And variance of the forecast
Page 333 and 334: CREATE COLUMN TABLE PAL_ARIMA_PDATA
Page 335 and 336: Table Column Column Data Type Descr
Page 337 and 338: INSERT INTO PAL_ARIMAX_DATA_TBL VAL
Page 339 and 340: 3.5.2 Auto ARIMA This function auto
Page 345 and 346: ● USER1 has been assigned the AFL
Page 347 and 348: CREATE LOCAL TEMPORARY COLUMN TABLE
Page 349 and 350: Example 3: Auto Seasonal ARIMA SET
Page 351 and 352: INSERT INTO PAL_ARIMA_DATA_TBL VALU
Page 353 and 354: INSERT INTO PAL_ARIMA_DATA_TBL VALU
Page 355 and 356: F 1 = a 0 + b 0 2. Calculation: S t
Page 357 and 358: Table 263: Name Data Type Default V
Page 359: INSERT INTO PAL_BROWNSMOOTH_DATA_TB
Page 365 and 366: SELECT * FROM PAL_CROSTON_STATISTIC
Page 367 and 368: Table 271: Name Data Type Descripti
Page 369 and 370: PAL_FORECASTACCURACYMEASURES_RESULT
Page 371 and 372: Table 275: Name Data Type Descripti
Page 375 and 376: Table 277: Table Column Column Data
Page 377 and 378: PAL_OUTPARAMETER_TBL: PAL_FORECASTS
Page 379 and 380: PAL_OUTPARAMETER_TBL: SAP HANA Pred
Page 381 and 382: CREATE TYPE PAL_CONTROL_T AS TABLE
Page 383 and 384: PAL_FORECASTTRIPLESMOOTHING_RESULT_
Page 385 and 386: PAL_OUTPARAMETER_TBL: PAL_FORECASTM
Page 387 and 388: PAL_OUTPARAMETER_TBL: PAL_FORECASTM
Page 389 and 390: Procedure Calling CALL .(, , , ) wi
Page 393 and 394: PAL_FORECASTSLR_RESULT_TBL: PAL_FOR
Page 395 and 396: Note The algorithm is backward comp
Page 399 and 400: INSERT INTO #PAL_CONTROL_TBL VALUES
Page 401 and 402: Note F 0 is not defined because the
Page 405 and 406: CREATE COLUMN TABLE PAL_DOUBLESMOOT
Page 407 and 408: 3.5.10 Triple Exponential Smoothing
Page 409 and 410: PAL calculates the prediction inter
Page 411 and 412:
Page 413 and 414:
Output Table Table 294: Table Colum
Page 415 and 416:
PAL_TRIPLESMOOTH_RESULT_TBL: PAL_TR
Page 417 and 418:
Once the trend and seasonality are
Page 419 and 420:
Page 421 and 422:
CREATE COLUMN TABLE PAL_TSSEASONALI
Page 423 and 424:
The difference-sign test counts the
Page 425 and 426:
Optional Parameters The following p
Page 427 and 428:
PAL_TSTREND_TREND_TBL: PAL_TSTREND_
Page 429 and 430:
Parameter Table Mandatory Parameter
Page 431 and 432:
3.6 Preprocessing Algorithms The re
Page 433 and 434:
● ● Smoothing by bin medians: e
Page 435 and 436:
Page 437 and 438:
3.6.2 Binning Assignment Binning as
Page 439 and 440:
Page 441 and 442:
3.6.3 Convert Category Type to Bina
Page 443 and 444:
"NAME" VARCHAR(100), "INTARGS" INTE
Page 445 and 446:
Procedure Generation CALL SYS.AFLLA
Page 447 and 448:
INSERT INTO PAL_IQR_TESTDT_TBL VALU
Page 449 and 450:
PARTITION This function reads the i
Page 451 and 452:
Page 453 and 454:
3.6.6 Posterior Scaling Posterior s
Page 455 and 456:
Page 457 and 458:
Expected Result PAL_NEW_SCALING_TBL
Page 459 and 460:
Page 461 and 462:
INSERT INTO PAL_PCA_DATA_TBL VALUES
Page 463 and 464:
Page 465 and 466:
INSERT INTO PAL_PCAPROJ_PDATA_TBL V
Page 467 and 468:
Page 469 and 470:
INSERT INTO PAL_DISTRRANDOM_PDATA_T
Page 471 and 472:
Table 347: Name Data Type Descripti
Page 473 and 474:
INSERT INTO PAL_SAMPLING_DATA_TBL V
Page 475 and 476:
If method is 4 and SAMPLING_SIZE is
Page 477 and 478:
DROP TABLE PAL_SAMPLING_PDATA_TBL;
Page 479 and 480:
Expected Result 3.6.10 Scaling Rang
Page 481 and 482:
Page 483 and 484:
Page 485 and 486:
Prerequisite Each column must conta
Page 487 and 488:
Page 489 and 490:
PAL_MISSING_VALUES_RESULT_TBL: PAL_
Page 491 and 492:
Page 493 and 494:
INSERT INTO PAL_VT_DATA_TBL VALUES
Page 495 and 496:
Page 497 and 498:
3.7.2 Chi-Squared Test for Independ
Page 499 and 500:
Page 501 and 502:
DISTRPROB This function calculates
Page 503 and 504:
Page 505 and 506:
Page 507 and 508:
"VALUE" DOUBLE ); DROP TABLE PDATA_
Page 509 and 510:
Page 511 and 512:
"DOUBLEARGS" DOUBLE, "STRINGARGS" V
Page 513 and 514:
"TYPE_NAME" NVARCHAR(256), "PARAMET
Page 515 and 516:
2. Calculate PAL also supports the
Page 517 and 518:
Page 519 and 520:
The Kaplan-Meier estimator can be r
Page 521 and 522:
Page 523 and 524:
Page 525 and 526:
PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
Page 527 and 528:
Page 529 and 530:
Page 531 and 532:
CREATE TYPE PAL_DISTRQUANTILE_RESUL
Page 533 and 534:
where x' = x - Table 404: Definitio
Page 535 and 536:
Page 537 and 538:
Let the sample mean of x and y be:
Page 539 and 540:
CREATE TYPE PAL_VAREQUALTEST_DATA1_
Page 541 and 542:
The quantity is computed as a weigh
Page 543 and 544:
Page 545 and 546:
Prerequisites ● ● Input data ca
Page 547 and 548:
"PARAMETER_TYPE" VARCHAR(7) ); INSE
Page 549 and 550:
Signature Input Tables Table 425: T
Page 551 and 552:
CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
Page 553 and 554:
"DISTANCE" DOUBLE ); DROP TYPE PAL_
Page 555 and 556:
"STRINGARGS" VARCHAR(100) ); --crea
Page 557 and 558:
The projected estimates are single
Page 559 and 560:
INSERT INTO PDATA VALUES (2, 'DM_PA
Page 561 and 562:
----product revenue---- DELETE FROM
Page 563 and 564:
---- calculate net present value of
Page 565 and 566:
UPDATE PAL_DISTRPROB_DISTRPARAM_TBL
Page 567 and 568:
Then we get the survival estimates
Page 569 and 570:
INSERT INTO PAL_TRIAL_DATA_TBL VALU
Page 571 and 572:
INSERT INTO PAL_DISTRFITCENSORED_DA
Page 573 and 574:
The expected results are as follows
Page 575 and 576:
); INSERT INTO PAL_DISTRPROB_PDATA_
Page 577 and 578:
Important Disclaimers and Legal Inf
show all

SAP HANA Predictive Analysis Library (PAL)

Create successful ePaper yourself

Delete template?

Save as template?