SAP HANA Predictive Analysis Library (PAL)

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Table Column Column Data Type Description 2nd column Integer or double Output result 3rd column Integer or double Lower bound of prediction interval 1 4th column Integer or double Upper bound of prediction interval 1 5th column Integer or double Lower bound of prediction interval 2 6th column Integer or double Upper bound of prediction interval 2 Statistics (optional) 1st column Varchar or nvarchar Name of statistics 2nd column Double Value of statistics Example Assume that: ● ● ● DM_PAL is a schema belonging to USER1; and USER1 has been assigned the AFLPM_CREATOR_ERASER_EXECUTE role; and USER1 has been assigned the AFL__SYS_AFL_AFLPAL_EXECUTE or AFL__SYS_AFL_AFLPAL_EXECUTE_WITH_GRANT_OPTION role. SET SCHEMA DM_PAL; DROP TYPE PAL_SINGLESMOOTH_DATA_T; CREATE TYPE PAL_SINGLESMOOTH_DATA_T AS TABLE("ID" INT, "RAWDATA" DOUBLE); DROP TYPE PAL_CONTROL_T; CREATE TYPE PAL_CONTROL_T AS TABLE ("NAME" VARCHAR(100), "INTARGS" INT, "DOUBLEARGS" DOUBLE, "STRINGARGS" VARCHAR(100)); DROP TYPE PAL_SINGLESMOOTH_RESULT_T; CREATE TYPE PAL_SINGLESMOOTH_RESULT_T AS TABLE ("TIME" INT, "OUTPUT" DOUBLE, "LOWER1" DOUBLE, "UPPER1" DOUBLE, "LOWER2" DOUBLE, "UPPER2" DOUBLE); DROP TYPE PAL_SINGLESMOOTH_STATISTICS_T; CREATE TYPE PAL_SINGLESMOOTH_STATISTICS_T AS TABLE ("NAME" VARCHAR (50), "VALUE" DOUBLE); DROP TABLE PAL_SINGLESMOOTH_PDATA_TBL; CREATE COLUMN TABLE PAL_SINGLESMOOTH_PDATA_TBL("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); INSERT INTO PAL_SINGLESMOOTH_PDATA_TBL VALUES (1,'DM_PAL', 'PAL_SINGLESMOOTH_DATA_T','IN'); INSERT INTO PAL_SINGLESMOOTH_PDATA_TBL VALUES(2,'DM_PAL', 'PAL_CONTROL_T','IN'); INSERT INTO PAL_SINGLESMOOTH_PDATA_TBL VALUES(3,'DM_PAL', 'PAL_SINGLESMOOTH_RESULT_T','OUT'); INSERT INTO PAL_SINGLESMOOTH_PDATA_TBL VALUES(4,'DM_PAL', 'PAL_SINGLESMOOTH_STATISTICS_T','OUT'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL', 'SINGLESMOOTH_TEST_PROC'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'SINGLESMOOTH', 'DM_PAL', 'SINGLESMOOTH_TEST_PROC',PAL_SINGLESMOOTH_PDATA_TBL); DROP TABLE #PAL_CONTROL_TBL; CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL ("NAME" VARCHAR(100), "INTARGS" INT, "DOUBLEARGS" DOUBLE, "STRINGARGS" VARCHAR(100)); INSERT INTO #PAL_CONTROL_TBL VALUES ('ADAPTIVE_METHOD',0, NULL, NULL); INSERT INTO #PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'MSE'); 398 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
INSERT INTO #PAL_CONTROL_TBL VALUES ('ALPHA', NULL,0.1, NULL); INSERT INTO #PAL_CONTROL_TBL VALUES ('DELTA', NULL,0.2, NULL); INSERT INTO #PAL_CONTROL_TBL VALUES ('FORECAST_NUM',6, NULL,NULL); INSERT INTO #PAL_CONTROL_TBL VALUES ('EXPOST_FLAG',1, NULL,NULL); INSERT INTO #PAL_CONTROL_TBL VALUES ('PREDICTION_CONFIDENCE_1', NULL, 0.8, NULL); INSERT INTO #PAL_CONTROL_TBL VALUES ('PREDICTION_CONFIDENCE_2', NULL, 0.95, NULL); DROP TABLE PAL_SINGLESMOOTH_DATA_TBL; CREATE COLUMN TABLE PAL_SINGLESMOOTH_DATA_TBL LIKE PAL_SINGLESMOOTH_DATA_T ; INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (1,200.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (2,135.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (3,195.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (4,197.5); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (5,310.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (6,175.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (7,155.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (8,130.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (9,220.0); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (10,277.5); INSERT INTO PAL_SINGLESMOOTH_DATA_TBL VALUES (11,235.0); DROP TABLE PAL_SINGLESMOOTH_RESULT_TBL; CREATE COLUMN TABLE PAL_SINGLESMOOTH_RESULT_TBL LIKE PAL_SINGLESMOOTH_RESULT_T ; DROP TABLE PAL_SINGLESMOOTH_STATISTICS_TBL; CREATE COLUMN TABLE PAL_SINGLESMOOTH_STATISTICS_TBL LIKE PAL_SINGLESMOOTH_STATISTICS_T; CALL DM_PAL.SINGLESMOOTH_TEST_PROC(PAL_SINGLESMOOTH_DATA_TBL, "#PAL_CONTROL_TBL", PAL_SINGLESMOOTH_RESULT_TBL, PAL_SINGLESMOOTH_STATISTICS_TBL) WITH OVERVIEW; SELECT * FROM PAL_SINGLESMOOTH_RESULT_TBL; SELECT * FROM PAL_SINGLESMOOTH_STATISTICS_TBL; Expected Result PAL_SINGLESMOOTH_RESULT_TBL: SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 399
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SAP HANA Platform SPS 12 Document V
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Naive Bayes. ......................
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1 What is PAL? SAP HANA’s SQLScri
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2 Getting Started with PAL This sec
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The result will tell you whether th
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● : The name of the schema where
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CREATE COLUMN TABLE PAL_ARIMAX_DATA
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For more information on how to use
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Category PAL Algorithm Built-in Fun
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Category PAL Algorithm Built-in Fun
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The procedure name is the same as s
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● USER1 has been assigned the AFL
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3.1.2 Agglomerate Hierarchical Clus
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Table 13: Name Data Type Default Va
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Name Data Type Default Value Descri
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INSERT INTO DATA_TBL VALUES ('17',
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Prerequisites ● ● The input dat
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INSERT INTO PAL_AD_DATA_TBL VALUES
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The signature table should contain
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CREATE TYPE PAL_KMEANS_CENTERS_T AS
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PAL_CLUSTER_ASSIGNED_TBL: For DBSCA
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For SOM: SET SCHEMA DM_PAL; DROP TY
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PAL_CLUSTER_ASSIGNED_TBL: Related I
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Table 24: Name Data Type Descriptio
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DROP TYPE PAL_CONTROL_T; CREATE TYP
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3.1.6 Gaussian Mixture Model (GMM)
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Table 29: Name Data Type Descriptio
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Output Tables Output format 0: Tabl
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INSERT INTO PAL_GMM_PDATA_TBL VALUE
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Example 2 SET SCHEMA DM_PAL; DROP T
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PAL_GMM_RESULTSMODEL_TBL: 3.1.7 K-M
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Table 43: Table Column Column Data
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INSERT INTO #PAL_CONTROL_TBL VALUES
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PAL_KMEANS_MODEL_TBL: VALIDATEKMEAN
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"V000" DOUBLE, "A0" INTEGER, "A1" I
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Expected Result PAL_SILHOUETTE_RESU
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Table Column Column Data Type Descr
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Example Assume that: ● ● ● DM
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PAL_KMEDIANS_ASSIGN_TBL: 3.1.9 K-Me
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Position Schema Name Table Type Nam
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The input, parameter, and output ta
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INSERT INTO PARAMETERS_TBL VALUES (
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DOCTOPICDIST_TBL: GENERALINFO_TBL:
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Procedure Calling CALL .(, , ) with
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Expected Result DOCTOPICDIST_TBL: 3
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The following parameters are option
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CREATE TYPE PAL_SOM_MODEL_T AS TABL
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PAL_SOM_RESASSIGN_TBL: PAL_SOM_MODE
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INSERT INTO PAL_SLIGHT_SIL_PDATA_TB
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AUC Procedure Generation CALL SYS.A
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Table 85: Name Data Type Default Va
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Example 2 SET SCHEMA DM_PAL; DROP T
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3.2.2 Back Propagation Neural Netwo
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CREATEBPNN This function trains a B
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INSERT INTO PAL_TRAIN_NN_DATA_TBL V
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PAL_TRAIN_NN_RESULT_TBL: PAL_REGRES
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Classification example: SET SCHEMA
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"NAME" VARCHAR(100), "MODEL" CLOB )
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CREATE TYPE PAL_C45_DATA_T AS TABLE
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CART This function is used for clas
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Prerequisites ● ● The target co
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PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
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INSERT INTO PAL_CM_DATA_TBL VALUES(
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PAL_KNN_RESULTS_TBL: 3.2.8 Logistic
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Procedure Calling CALL .(, , , , )
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PAL_LOGISTICR_STAT_TBL: PAL_LOGISTI
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Procedure Generation CALL SYS.AFLLA
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Assume that: ● ● ● DM_PAL is
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PAL_FLOGISTICR_FITTED_TBL: Related
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Expected Result PAL_LRMC_MODEL_TBL:
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Table 137: Name Data Type Default V
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SAP HANA Predictive Analysis Librar
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CALL SYS.AFLLANG_WRAPPER_PROCEDURE_
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3.2.10 Naive Bayes Naive Bayes is a
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● ● USER1 has been assigned the
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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Position Schema Name Table Type Nam
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DROP TABLE CV_LR_FUNC_TBL; CREATE C
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CREATE COLUMN TABLE FUNC_TBL LIKE F
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DROP TABLE EVALUATION_RESULT_TBL; C
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INSERT INTO PAL_PARA_TBL VALUES('TR
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3.2.13 Random Forest The ransom for
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INSERT INTO PAL_RF_DATA_TBL VALUES
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3.2.14 Support Vector Machine Suppo
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Table 168: Name Data Type Descripti
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CREATE TABLE PAL_SVM_PDATA_TBL("POS
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STRINGARGS VARCHAR(100) ); DROP TYP
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Example 3: Support vector classific
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Related Information SAP HANA Smart
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INSERT INTO PAL_GR_PDATA_TBL values
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Procedure Calling CALL .(, , , ) wi
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3.3.2 Bi-Variate Natural Logarithmi
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CREATE LOCAL TEMPORARY COLUMN TABLE
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Where β0…βn are parameters that
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CREATE LOCAL TEMPORARY COLUMN TABLE
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Assume we have m observation pairs
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Example 1: Fitting multiple linear
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CREATE COLUMN TABLE PAL_ENET_MLR_PD
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3.3.5 Polynomial Regression Polynom
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insert into PAL_PR_PDATA_TBL values
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The input, parameter, and output ta
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3.4 Association Algorithms This sec
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"STRINGARGS" VARCHAR (100) ); INSER
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Optional Parameters The following p
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INSERT INTO PAL_APRIORI_DATA_TBL VA
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PAL_APRIORI_CONS_ITEMS_TBL: LITEAPR
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INSERT INTO PAL_LITEAPRIORI_DATA_TB
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FPGROWTH (Relational Output) FP-Gro
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); DROP TYPE PAL_FPGROWTH_POSTRULE_
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PAL_FPGROWTH_VALUES_TBL: 3.4.3 K-Op
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INSERT INTO PAL_KORD_DATA_TBL VALUE
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Prerequisite No missing or null dat
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. Initial P 0 can be calculated by
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Output Table (Model Table) Table 24
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DROP TYPE PAL_ARIMAX_DATA_T; CREATE
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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We get And variance of the forecast
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CREATE COLUMN TABLE PAL_ARIMA_PDATA
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INSERT INTO PAL_ARIMAX_DATA_TBL VAL
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3.5.2 Auto ARIMA This function auto
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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
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Page 355 and 356: F 1 = a 0 + b 0 2. Calculation: S t
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Page 359 and 360: INSERT INTO PAL_BROWNSMOOTH_DATA_TB
Page 361 and 362: 3.5.4 Croston's Method The Croston
Page 363 and 364: Name Data Type Default Value Descri
Page 365 and 366: SELECT * FROM PAL_CROSTON_STATISTIC
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Page 369 and 370: PAL_FORECASTACCURACYMEASURES_RESULT
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Page 375 and 376: Table 277: Table Column Column Data
Page 377 and 378: PAL_OUTPARAMETER_TBL: PAL_FORECASTS
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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
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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 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
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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 437 and 438: 3.6.2 Binning Assignment Binning as
Page 439 and 440: Table Column Column Data Type Descr
Page 441 and 442: 3.6.3 Convert Category Type to Bina
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Page 445 and 446: Procedure Generation CALL SYS.AFLLA
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PARTITION This function reads the i
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3.6.6 Posterior Scaling Posterior s
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Expected Result PAL_NEW_SCALING_TBL
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INSERT INTO PAL_PCA_DATA_TBL VALUES
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INSERT INTO PAL_PCAPROJ_PDATA_TBL V
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INSERT INTO PAL_DISTRRANDOM_PDATA_T
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Table 347: Name Data Type Descripti
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INSERT INTO PAL_SAMPLING_DATA_TBL V
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If method is 4 and SAMPLING_SIZE is
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DROP TABLE PAL_SAMPLING_PDATA_TBL;
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Expected Result 3.6.10 Scaling Rang
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Prerequisite Each column must conta
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PAL_MISSING_VALUES_RESULT_TBL: PAL_
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INSERT INTO PAL_VT_DATA_TBL VALUES
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3.7.2 Chi-Squared Test for Independ
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DISTRPROB This function calculates
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"VALUE" DOUBLE ); DROP TABLE PDATA_
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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"TYPE_NAME" NVARCHAR(256), "PARAMET
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2. Calculate PAL also supports the
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The Kaplan-Meier estimator can be r
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PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
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CREATE TYPE PAL_DISTRQUANTILE_RESUL
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where x' = x - Table 404: Definitio
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Let the sample mean of x and y be:
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CREATE TYPE PAL_VAREQUALTEST_DATA1_
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The quantity is computed as a weigh
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Prerequisites ● ● Input data ca
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"PARAMETER_TYPE" VARCHAR(7) ); INSE
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Signature Input Tables Table 425: T
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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"DISTANCE" DOUBLE ); DROP TYPE PAL_
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"STRINGARGS" VARCHAR(100) ); --crea
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The projected estimates are single
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INSERT INTO PDATA VALUES (2, 'DM_PA
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----product revenue---- DELETE FROM
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---- calculate net present value of
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UPDATE PAL_DISTRPROB_DISTRPARAM_TBL
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Then we get the survival estimates
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INSERT INTO PAL_TRIAL_DATA_TBL VALU
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INSERT INTO PAL_DISTRFITCENSORED_DA
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The expected results are as follows
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); INSERT INTO PAL_DISTRPROB_PDATA_
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Important Disclaimers and Legal Inf
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SAP HANA Predictive Analysis Library (PAL)

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