SAP HANA Predictive Analysis Library (PAL)

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"VALUE" DOUBLE ); DROP TABLE PAL_FORECASTACCURACYMEASURES_DATA_TBL; CREATE COLUMN TABLE PAL_FORECASTACCURACYMEASURES_DATA_TBL("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (1,'DM_PAL', 'PAL_FORECASTACCURACYMEASURES_DATA_T','IN'); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES(2,'DM_PAL', 'PAL_CONTROL_T','IN'); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES(3,'DM_PAL', 'PAL_FORECASTACCURACYMEASURES_RESULT_T','OUT'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL', 'PAL_FORECASTACCURACYMEASURES_PROC'); CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'ACCURACYMEASURES', 'DM_PAL', 'PAL_FORECASTACCURACYMEASURES_PROC',PAL_FORECASTACCURACYMEASURES_DATA_TBL); DROP TABLE PAL_FORECASTACCURACYMEASURES_DATA_TBL; CREATE COLUMN TABLE PAL_FORECASTACCURACYMEASURES_DATA_TBL LIKE PAL_FORECASTACCURACYMEASURES_DATA_T; INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (1130, 1270); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2410, 2340); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2210, 2310); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2500, 2340); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2432, 2348); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (1980, 1890); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2045, 2100); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2340, 2231); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2460, 2401); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2350, 2310); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2345, 2340); INSERT INTO PAL_FORECASTACCURACYMEASURES_DATA_TBL VALUES (2650, 2560); DROP TABLE PAL_CONTROL_TBL; CREATE COLUMN TABLE PAL_CONTROL_TBL( "NAME" VARCHAR (50), "INTARGS" INTEGER, "DOUBLEARGS" DOUBLE, "STRINGARGS" VARCHAR (100) ); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'MSE'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'RMSE'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'MPE'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'ET'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'MAD'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'MASE'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'WMAPE'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'SMAPE'); INSERT INTO PAL_CONTROL_TBL VALUES ('MEASURE_NAME', NULL, NULL, 'MAPE'); INSERT INTO PAL_CONTROL_TBL VALUES ('IGNORE_ZERO', 1, NULL, NULL); DROP TABLE PAL_FORECASTACCURACYMEASURES_RESULT_TBL; CREATE COLUMN TABLE PAL_FORECASTACCURACYMEASURES_RESULT_TBL LIKE PAL_FORECASTACCURACYMEASURES_RESULT_T; CALL DM_PAL.PAL_FORECASTACCURACYMEASURES_PROC(PAL_FORECASTACCURACYMEASURES_DATA_TBL, PAL_CONTROL_TBL, PAL_FORECASTACCURACYMEASURES_RESULT_TBL) WITH OVERVIEW; SELECT * FROM PAL_FORECASTACCURACYMEASURES_RESULT_TBL; Expected Result 368 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
PAL_FORECASTACCURACYMEASURES_RESULT_TBL: 3.5.6 Forecast Smoothing Forecast smoothing is used to calculate optimal parameters of a set of smoothing functions in PAL, including Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing. This function also outputs the forecasting results based on these optimal parameters. This optimization is computed by exploring of the parameter space which includes all possible parameter combinations. The quality assessment is done by comparing historic and forecast values. In PAL, MSE (mean squared error) or MAPE (mean absolute percentage error) is used to evaluate the quality of the parameters. The parameter optimization is based on global and local search algorithms. The global search algorithm used in this function is simulated annealing, whereas the local search algorithm is Nelder Mead. Those algorithms allow for efficient search processes. To evaluate the flexibility of the function, a train-and-test scheme is carried out. In other words, a partition of the time series is allowed, of which the former one is used to train the parameters, whereas the latter one is applied to test. The error of every forecast value is calculated as follows: D i =(1+i* ∝ 2 )*E Where ∝ is the weight of smoothing and E is the calculated MSE or MAPE value. Prerequisites ● ● No missing or null data in the inputs. The data is numeric, not categorical. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 369
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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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Page 319 and 320: INSERT INTO PAL_KORD_DATA_TBL VALUE
Page 321 and 322: Prerequisite No missing or null dat
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Page 325 and 326: Output Table (Model Table) Table 24
Page 327 and 328: DROP TYPE PAL_ARIMAX_DATA_T; CREATE
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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
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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
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Page 377 and 378: PAL_OUTPARAMETER_TBL: PAL_FORECASTS
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Page 381 and 382: CREATE TYPE PAL_CONTROL_T AS TABLE
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Page 385 and 386: PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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Page 407 and 408: 3.5.10 Triple Exponential Smoothing
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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_
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Parameter Table Mandatory Parameter
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3.6 Preprocessing Algorithms The re
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● ● Smoothing by bin medians: e
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3.6.2 Binning Assignment Binning as
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3.6.3 Convert Category Type to Bina
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"NAME" VARCHAR(100), "INTARGS" INTE
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Procedure Generation CALL SYS.AFLLA
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INSERT INTO PAL_IQR_TESTDT_TBL VALU
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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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