SAP HANA Predictive Analysis Library (PAL)

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Category PAL Algorithm Built-in Function Name K-Optimal Rule Discovery (KORD) [page 315] KORD Time Series ARIMA [page 320] ARIMATRAIN ARIMAFORECAST ARIMAXFORECAST Auto ARIMA [page 339] Brown Exponential Smoothing [page 354] Croston's Method [page 361] Forecast Accuracy Measures [page 365] Forecast Smoothing [page 369] Linear Regression with Damped Trend and Seasonal Adjust [page 388] Single Exponential Smoothing [page 394] Double Exponential Smoothing [page 400] Triple Exponential Smoothing [page 407] Seasonality Test [page 416] Trend Test [page 422] White Noise Test [page 427] AUTOARIMA BROWNEXPSMOOTH CROSTON ACCURACYMEASURES FORECASTSMOOTHING LRWITHSEASONALADJUST SINGLESMOOTH DOUBLESMOOTH TRIPLESMOOTH SEASONALITYTEST TRENDTEST WHITENOISETEST Preprocessing Binning [page 431] BINNING Binning Assignment [page 437] Convert Category Type to Binary Vector [page 441] Inter-Quartile Range Test [page 444] Partition [page 448] Posterior Scaling [page 453] BINNINGASSIGNMENT CONV2BINARYVECTOR IQRTEST PARTITION POSTERIORSCALING 18 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
Category PAL Algorithm Built-in Function Name Principal Component Analysis (PCA) [page 457] PCA PCAPROJECTION Random Distribution Sampling [page 465] DISTRRANDOM Sampling [page 469] SAMPLING Scaling Range [page 479] SCALINGRANGE Substitute Missing Values [page 484] SUBSTITUTE_MISSING_VALUES Variance Test [page 489] VARIANCETEST Statistics Chi-Squared Test for Goodness of Fit [page 494] CHISQTESTFIT Chi-Squared Test for Independent [page 497] CHISQTESTIND Cumulative Distribution Function [page 500] DISTRPROB Distribution Fitting [page 504] DISTRFIT DISTRFITCENSORED Grubbs' Test [page 514] GRUBBSTEST Kaplan-Meier Survival Analysis [page 518] KMSURV Multivariate Statistics [page 525] MULTIVARSTAT Quantile Function [page 528] DISTRQUANTILE Univariate Statistics [page 532] UNIVARSTAT Variance Equal Test [page 536] VAREQUALTEST Social Network Analysis Link Prediction [page 540] LINKPREDICTION Miscellaneous ABC Analysis [page 544] ABC Weighted Score Table [page 547] WEIGHTEDTABLE SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 19
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: 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 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: The signature table should contain
Page 69 and 70:
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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Name Data Type Default Value Descri
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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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The signature table should contain
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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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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
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
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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
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INSERT INTO PAL_ARIMAX_DATA_TBL VAL
Page 339 and 340:
3.5.2 Auto ARIMA This function auto
Page 341 and 342:
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● USER1 has been assigned the AFL
Page 347 and 348:
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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INSERT INTO PAL_ARIMA_DATA_TBL VALU
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F 1 = a 0 + b 0 2. Calculation: S t
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INSERT INTO PAL_BROWNSMOOTH_DATA_TB
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3.5.4 Croston's Method The Croston
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SELECT * FROM PAL_CROSTON_STATISTIC
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PAL_FORECASTACCURACYMEASURES_RESULT
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PAL_OUTPARAMETER_TBL: PAL_FORECASTS
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PAL_OUTPARAMETER_TBL: SAP HANA Pred
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CREATE TYPE PAL_CONTROL_T AS TABLE
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PAL_FORECASTTRIPLESMOOTHING_RESULT_
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
Page 389 and 390:
Procedure Calling CALL .(, , , ) wi
Page 391 and 392:
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PAL_FORECASTSLR_RESULT_TBL: PAL_FOR
Page 395 and 396:
Note The algorithm is backward comp
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Note F 0 is not defined because the
Page 403 and 404:
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CREATE COLUMN TABLE PAL_DOUBLESMOOT
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3.5.10 Triple Exponential Smoothing
Page 409 and 410:
PAL calculates the prediction inter
Page 411 and 412:
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Output Table Table 294: Table Colum
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PAL_TRIPLESMOOTH_RESULT_TBL: PAL_TR
Page 417 and 418:
Once the trend and seasonality are
Page 419 and 420:
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CREATE COLUMN TABLE PAL_TSSEASONALI
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The difference-sign test counts the
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Optional Parameters The following p
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PAL_TSTREND_TREND_TBL: PAL_TSTREND_
Page 429 and 430:
Parameter Table Mandatory Parameter
Page 431 and 432:
3.6 Preprocessing Algorithms The re
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● ● Smoothing by bin medians: e
Page 435 and 436:
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3.6.2 Binning Assignment Binning as
Page 439 and 440:
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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:
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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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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
Page 487 and 488:
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PAL_MISSING_VALUES_RESULT_TBL: PAL_
Page 491 and 492:
The procedure name is the same as s
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INSERT INTO PAL_VT_DATA_TBL VALUES
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The procedure name is the same as s
Page 497 and 498:
3.7.2 Chi-Squared Test for Independ
Page 499 and 500:
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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
Page 517 and 518:
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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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