Weka Time Series Forecast, More Attributes - data-mining

According to this link, it's explained how to create a forecast model based on date field and measures.
It's working in case when I do not put any other attributes. I need to forward more fields, like SKU no, PointOfSales no etc.
In those cases it returns me an error with 'bad sorting' message even it is sorted asc by date field.
Any help? Am I missing something simple?
EDIT:
working table:
DateField TIMESTAMP
Sales FLOAT
Non working table:
DateField TIMESTAMP
Sales FLOAT
SKU VARCHAR
PoS VARCHAR
Non-working ARFF sample
#relation 'C:\\Users\\admpentaho\\Desktop\\file1.arff'
#attribute dat_dok date 'yyyy/MM/dd HH:mm:ss.SSS'
#attribute sif_rob {0051S12,0312S13,0762S21,160047,160049,160051,160058,2101S11,250000,250001,250002,250003,250006,250007,250008,2562S12,280001,280002,280003,280004,280005,280006,280007,280008,280009,280010,280011,280012,280013,280014,280015,280017,280018,280019,280020,280021,280022,280023,280024,280027,280028,280043,280044,280045,280047,280049,280050,280053,'2810 S12',3081S21,370002,370003,370007,370015,50004,50005,50006,50007,50008,50015,50018,50019,50023,50034,50070,50073,50076,50077,50101,50103,50112,50116,50118,50138,50148,50149,50161,50185,50186,50196,50198,50199,50203,50204,50236,50238,50239,50240,50241,50242,50243,50244,50245,50246,50251,50252,50264,50272,50273,50274,50279,50295,50296,50299,50307,50308,70001,70003,70005,70006,70007,70008,70013,70014,70015,70016,70017,70018,70028,70029,70032,70033,70035,70039,70040,70042,70043,70044,70046,70048,70049,70065,70070,70073,70074,70078,70079,70085,70087,70088,70089,70092,70096,70097,70098,70100,70136,70137,70144,70156,70167,70168,70169,70241,702426,70243,70245,79003,900327,900334,900336,900353,900354,900356,900362,ALP3150,ALP3151,AM12012,AM12013,AM12014,AM12015,AM12016,AM12017,AM12018,AM12019,AM12050,AMAN57788,AMAN58725,BER3044,BER3047,BER3048,BER3054,BER3070,BIS10001,BIS10108,BIS10223,BIS10224,BIS10289,BIS10307,BIS10309,BIS10313,BIS10315,BIS10362,BIS10369,BIS10370,BIS10371,BIS10379,BIS10380,BIS11107,BIS11208,BIS11236,BIS11237,BIS11238,BIS11304,BIS11306,BIS11320,BIS11327,BIS12015,BIS12018,BIS12072,BIS12074,BIS12076,BIS12078,BIS12079,BIS12080,BIS12081,BIS12083,BIS12084,BIS12091,CAS3372,CAS3376,CAS3377,CAS3378,CAS3379,CE001,CE002,CE003,CE004,CE005,CE106348,CE701850,CE701852,DE0FX3176,DE0SI0012,DE0SI1034,DE0SI1041,DE0SW0209,DE0SX0007,DE0SX0011,DE0SX0012,DE0SX0015,DE0SX0034,DE0SX0041,DE0SX1093,DE0UA2112,DE0UD2100,DE0UN2103,DE0UN2104,DEGN20GN1,DEPA10985,DEPPE1976,DERI10850,DERI40853,DERI50854,DERI60855,DESE26919,DESG86916,DESOF4001,DESOL1001,DESOL2002,DMK3130,DMK3131,DMK3132,DMK3133,DMK8008,DON0006,DON0007,DON0008,DON0013,DON0016,E0111461,E0111463,E0111466,E0111505,E0111506,E0112153D,E0112215,E0112353,E0112488,E0112535,E0112536,E100378,E101119,E101280,E101327,E101331,E101599,E101661,E101661D,E101661K,E101685,E102033,E103896,E105309,E106347,E106348,E106353,E106354,E106356,E106357,E106358,E106359,E106360,E108758,E108759,E1100834,E1100912,E1101523,E1103885,E1104176,E1104579,E1105041,E1106190,E1106349,E1106350,E1106361,E1106362,E1106363,E1106831,E1108159,E1108551,E1109364,E1109697,E1109747,E1111521,E1200368,E1202281,E1202799,E1203602,E1208085,E1208086,E1208662,E1211522,E1308084,E1501017,E1501144,E1501145,E1501146,E1501545,E1502172,E1505223,E1505854,E1507020,E1507948,E1507950,E1601055,E1704580,E1705080,E2006333,E700784,E701153,E701154,E701155,E701197,E701197D,E701596,E701787,E701850,E701850D,E701852,E701852D,E701856,E701856D,E701858,E702665,E704253,E706522,E707095,E707454,E707461,E707605,E707617,E707856,E708265,E708266,E708373,E708374,E708375,E708678,E708685,E708988,E708989,E708990,E709361,E709671,E709884,E710598,EPAL,EVB-2000,EVB-2001,EVB-303,EVB-411,EVB-412,EVB-413,EVB-414,EVB-416,EVB-418,EVB-421,EVB-422,EVB-424,EVB-500293,EVB-500294,EVB-500295,EVB-500296,EVB-500297,EVB-500329,EVB-500330,EVB-500331,EVB-500396,EVB-500397,EVB-500399,EVB-500400,EVB-500402,EVB-500403,EVB-500404,EVB-500405,EVB-500717,EVB-500750,EVB-500751,EVB-500752,EVB-500756,EVB-500778,EVB-500781,EVB-501075,EVB-501076,EVB-502130,EVB-502140,EVB-502290,EVB-502291,EVB-502552,EVB-502680,EVB-502681,EVB-502682,EVB-502683,EVB-502897,EVB-502953,EVB-503051,EVB-503251,EVB-503252,EVB-503255,EVB-503283,EVB-503284,EVB-503290,EVB-503291,EVB-503651,EVB-503654,EVB-503781,EVB-K411,EVB-K413,EVB-K421,EVB-K422,EVB-K424,FI31002,FI31011,FLA000021,FLA000027,FLA000028,FLA000030,FLA000031,FLA000033,FLA000066,FLA000067,FLA000075,FLA000083,FLA000084,FLA000087,FLA000089,FLA000091,FLA000111,FLA000112,FLA000133,FLA000138,FLA000241,FLA000242,FLA000243,FLA000279,FLA000330,FLA000331,FLA000386,FLA000487,FLA000589,FLA000715,FLA000839,FLA001164,FLA001165,FLA001223,FLA001225,FLA001226,FLA001363,FLA001474,FLA001475,FLA001888,FLA001949,FLA002289,FLA002291,FLA002292,FLA002431,FLA002763,FLA002764,FLA002792,FLA002921,FLA002922,FLA002923,FLA002934,FLA002940,FLA002944,FLA002946,FLA002948,FLA002952,FLA002956,FLA002958,FLA002961,FLA002965,FLA002967,FLA002969,FLA002983,FLA002985,FLA002987,FLA002989,FLA002991,FLA002993,FLA003001,FLA003009,FLA003024,FLA003026,FLA003028,FLA003031,FLA003035,FLA003039,FLA003045,FLA003050,FLA003052,FLA003089,FLA003181,FLA003182,FLA003183,FLA003184,FLA003186,FLA003214,FLA003215,FLA003216,FLA003273,FLA003283,FLA003320,FLA003330,FLA003331,FLA003333,FLA003334,FLA003527,FLA003601,FLA003728,FLA003729,FLA003730,FLA003731,FLA003916,FLA003917,FLA003919,FLA003927,FLA003934,FLA004105,FLA004129,FLA004130,FLA004196,FLA004244,FLA004299,FLA004300,FLA004478,FLA004479,FLA004497,FLA004535,FLA004536,FLA004537,FLA004538,FLA004539,FLA004540,FLA004542,FLA004543,FLA004544,FLA004545,FLA004550,FLA004551,FLA004552,FLA004553,FLA004599,FLA004608,FLA004614,FLA005674,FLA006086,FLA006087,FLA290001,FRS064,GE17919,GE19948,GE19949,GEP61504,GL110000,GL210000,GL340100,GL360200,GL360201,GOR1120,GOR1213,GOR1301,GOR1308,GOR1311,GOR1315,GOR1316,GOR1339,GOR1340,GOR1341,GOR1604,GOR1817,GOR1852,GOR1854,GOR1902,GOR1905,GOR1906,GOR1907,GOR1912,GOR1913,GOR1927,GOR1929,GOR1930,GOR1932,GOR1933,GOR1934,GOR1942,GOR1944,GOR1945,GOR1947,GOR1948,GOR1952,H1834230,H2013212,H2017280,H2017283,H2017288,H2295500,H2295800,H2298188,H2395100,H2395166,H2395166A,H2395250A,H2395520A,H2395540,H2395566,H2395840,H2395866,H2395866A,H2494131,H2494241,H2494311,H2496131,H2496241,H2496311,H2498131,H2498241,H2498311,H2499231,H2499241,H2499311,H2817541,H2818541,H2819541,H2943251,H2961001,H2961101,H2961201,HLS001,HOC5006,HOC5007,HOC5108,HOC6008,HOC6009,HOC9000,HOC9001,HOC9002,HOC9007,HOC9009,HOC9015,HUG016,HUG017,HUG019,HUG020,HUG1607,HUGLS0102,HUGLS0103,J1423204,J1764002,J1764003,J1855405,J2061704,J2079703,J2080503,J2103103,J2103303,J2103603,J2129506,J2133406,J2141110,J2142209,J2144402,J2148404,J2265102,J2279902,J2318705,J2364102,J2364202,J2364702,J2364802,J2364902,J2372202,J2375802,J2423819,J2466503,J2500800,J2584509,J2584706,J2589804,J3098916,J3098917,J3101902,J3106903,J3166005,J3184001,J3185101,J3185401,J3276504,J3276805,J3277102,J3309702,J3328304,J3333602,J3362404,J3365006,J3369805,J3382502,J3398204,J3519004,J3646716,J3660202,J3666000,J3666400,J3682507,J3699704,J3803900,J3809405,J3834607,J3835107,J3841200,J3854102,J3854402,J3854702,J3855002,J3887401,J3897402,J3898002,J3922901,J3954502,J3983101,J3995301,J4010601,J4023602,J4023702,J4069403,J4100102,J4145904,J4146303,J4272302,J4290002,J4292000,J4292701,J4293401,J4307300,J4313100,J4318700,J431900,J4319000,J4346101,J43709,J4394702,J4432601,J4458601,J4459701,J4501700,J4670402,J4730908,J4736310,J4748307,J4775203A,J4775304A,J4789400,J4789502,J4952004,J4985800,J4993200,J4993300,J5217309,J5217907,J5218006,J5218106,J5218405,J5337500,J5462500,J5479200,J5705100,J5705200,J5705300,J5768200,J5768300,J5787900,J5800300,J5805000,J5806105,J5852100,J5883001,J6008100,J6008400,J6037300,J6041600,J6046200,J6047400,J6057301,J6057401,J6127800,J6148800,J6212700,J6213300,J6213800,J6213900,J6214000,J6214100,J6214200,J6214300,J6214400,J6214500,J6214600,J6214700,J6214800,J6214900,J6215300,J6215400,J6215600,J6215800,J6216000,J6216100,J6216200,J6216300,J6216400,J6216600,J6216700,J6217000,J6246000,J6250800,J6281000,J6287000,J6306700,J6307300,J6308600,J6309100,J6354503,J6390201,J6394600,J6424300,J6424400,J6428503,J6428901,J6454700,J6460700,J6463100,J6468500,J6479400,J6479600,J6489501,J6504800,J6504900,J6638000,J6651100,J6667301,J6673203,J6686800,J6705700,J6764800,J6766002,J6766100,J6777800,J6849200,J6849300,J6849400,J6849600,J6849900,J6889701,J702226,J702227,J702228,J702229,J7023900,J7125300,J7125700,J7144900,J7188400,J7290900,J7324500,J7519300,J7567700,J7567800,J7650100,J7650400,J7650400D,J7653400,J7653400D,J7656600,J7693500,J7696700,J7706600D,J7759001,J7759101,J7869100,J8003704,J8046100,J8060201,J8060500,J8083500,J8083600,J8083701,J8083901,J8084001,J8084100,J8084201,J8084300,J8084400,J838704,J838810,JCC003,JJB033,JJB035,JJB036,JJB047,JJB048,JJB049,JJB050,JJB051,JJB052,JJJB0030,JJJB010,JJJB0106,JJJB0114,JJJB0118,JJJB012,JJJB0120,JJJB0121,JJJB0124,JJJB0125,JJJB013,JJJB019,JJJB020,JJJBO20135,JJOB020415,JJRX503204,JO14007,JO14008,JO14009,JO14010,JO14011,JO14016,JO14017,JO14018,JO14019,JO14050,JOB006,JRE003,JRE004,JRE007,JRE008,JRE010,K3086160,K3158150,K3262010,K3270661,K3270670,K3415800,K3417600,K3482121,K3519880,K3527090,K3568140,K3570140,K3571140,K3697800,K3698210,K3745030,K3824120,K3855270,K4521020,K4584040,K4700430,K4725040,K4729010,K4739350,K4762230,K4762240,K4845030,K4845040,K4933950,K4933960,K4934100,K4934110,K4961350,K4961360,K4961450,K4961800,K4961810,K5016385,K5027020,K5110510,K5115120,K5605000,K5606000,KLBT0107,KLBT0108,KLFT0105,MI14045,MI14046,MI14047,MI14048,MP00314,MP00317,MP00331,MP00419,MP00580,MP00834,MP00999,MP01064,MP01084,MP01098,MP01152,MP01154,MP01214,MP01247,MP01254,MP01321,MP01369,MP01435,MP01455,MP01456,MP01543,MP01584,MP01593,MP01595,MP01600,MP01601,MP01605,MP01606,MP01608,MP01640,MP01645,MP01653,MP01689,MP01694,MP01701,MP01865,MP01866,MP02051,MP02059,MP02126,MP02183,MP02408,MP02410,MP02420,MP02459,MP02491,MP02563,MP02610,MP02615,MP02936,MP02952,MP02968,MP02974,MP02977,MP03017,MP03020,MP03263,MP04125,MP04261,MP04321,MP04474,MP04703,MP05206,MP05313,MP05314,MP05460,MP05462,MP05716,MP05768,MP06144,MP06145,MP06198,MP06339,MP06349,MP06436,MP06466,MP06467,MP06468,MP06609,MP06610,MP07183,MP07248,MP07249,MP07290,MP07313,MP07673,MP07696,MP07702,MP07876,MP08113,MP08119,MP08128,MP08136,MP08225,MP08247,MP08248,MP08302,MP08318,MP08416,MP08474,MP08476,MP08962,MP09019,MP09336,MP09540,MP09558,MP10118,MP11079,MP11085,MP11259,MP11543,MP11560,MP11664,MS008,PCF001,PCF003,PCF004,PCF005,PCOR001,PEB001,PEB002,PEB003,PEB004,PEB005,PEB006,PEB013,PEB014,PEB018,PEB019,PEB020,PFR004,PFR007,PFR008,PFRS004,PFRS005,PFRS007,PHUG003,PHUG004,PHUG005,PJ&J103,PJ&J105,PJ&J106,PJ&J107,PJ&J108,PJ&J110,PJ&J113,PJ&J114,PJ3185401,PJ4010601,PJ7519300,PJB001,PJB006,PJB007,PJB009,PJB010,PJB011,PJB012,PJB013,PJB014,PJB015,PJB019,PLM001,PLM002,PNT001,PO0045,PO0046,POB001,POB002,POB003,POB0033,POB004,POB005,POB006,POB010,POB011,POB012,PPJ5800300,RE13207,RE13208,RE13209,RE13210,RE13211,RE13212,RE13304,RE13307,RE13308,RE13309,RE13310,RE13311,RH2395540,RJ5217907,RJ5218006,RJ5218106,RJ5218405,RJ5986705,RJ6213300,RJ6213800,RJ6214000,RJ6214600,RJ6215800,RJ7759101,RS0107,RSKO004,RSKO005,RSKO006,RSKO007,RSKO008,RSKO009,RSKO010,RSKO011,RSKO012,RSKO013,RSKO014,RSKO015,RSKO016,RSKO017,SAN1220,SAN2000,SAN2003,SAN2029,SAN2201,SAN2302,SAN2400,SAN2401,SAN2502,SAN3256,SAN5029,SKO0001,SKO1013,SKO1064,SKO1381,SKO1382,SKO1411,SKO161,SKO163,SKO164,SKO169,SKO171,SKO1718,SKO1720,SKO2093,SKO2094,SKO3320,SKO3564,SKO3603,SKO3683,SKO3729,SKO3730,SKO3812,SKO4194,SKO4272,SKO4281,SKO611,SKO612,SKO706,SKO711,SKO724,SKO777,SKO944,SKO988,SP13200,SP13202,SP13203,SP13204,SP13205,SP13206,SP13300,SP13301,SP13302,SP13303,SP13306,TEFS11E27,UNI12215721,UNI14402415,UNI14520114,UNI14520312,UNI15440220,UNI15440321,UNI15440422,UNI15667103,UNI16661001,UNI16874401,UNI16874601,UNI17112413,UNI17137701,UNI17137901,UNI17139805,UNI17276602,UNI17783401,UNI18304712,UNI19047102,UNI19102908,UNI19249808,UNI19418414,UNI19421510,UNI19423004,UNI19461610,UNI19656103,UNI19905101,UNI26704201,UNI39047202,V0001,V0002,V1050001,V1050002,V1051001,V1051002,V1051011,V1052001,V1052002,V1052003,V1052007,V1052008,V1052009,V1052010,V1052011,V1052045,V1052046,V1052047,V1052048,V1053001,V1053002,V1053004,V1054001,V1054002,V1054004,V1055001,V1055002,V1100012,V1100013,V1100014,V1155001,V1155002,V1250001,V1250002,V1250003,V1250004,V1250005,V1250006,V1251001,V1251002,V1251003,V1251004,V1251023,V1251024,V1251032,V1251038,V1253001,V1253002,V1253003,V1253004,V1253005,V1253006,V1253007,V1253008,V1253019,V1254002,V1254003,V1254004,V1255001,V1255002,V1255003,V1255004,V1545001,V1545002,V1545003,V1545004,V1545005,V1545007,V1758001,V1758002,V1758003,V1758004,V1859001,V1859002,V1859003,V1859004,V1860001,V1860002,V1860003,V1946001,V1946002,V1946003,V1946006,V1947001,V2061001,V2061002,V2061003,V2061004,V2061005,V2061006,V2365002,V2365003,V2700007,V2800001,V2800002,V2800013,V2800014,V3300051,V3887001,V3887002,VA001,VA002,VA003,VA4103101412,VA4103101414,VA4106101412,VA4106101414,VA4703101436,VA4706101436,VA4903121412,VA4903121414,VA4906121412,VA4906121414,VA56703101402,VA56706101402,VA57666101451,VIPC001,WES3021,WES3024,WES3026,WES3031,WES3035,WES3069,WES3079,WES3080,ZOT1000,ZOT1001,ZOT1002,ZOT1003,ZOT1004,ZOT1005,ZOT1006,ZOT1009,ZOT1011,ZOT1013,ZOT1014,ZOT1043,ZOT1044,ZOT1045,ZOT1046,ZOT16003,ZOT16004,ZOT16005,ZOT2000,ZOT2001,ZOT2010,ZOT2025,ZOT2028,ZOT2043,ZOT2045,ZOT2046,ZOT2047,ZOT2047D,ZOT2108,ZOT2110,ZOT2402,ZOT2403,ZOT2405,ZOT2406,ZOT2407,ZOT3003,ZOT3004,ZOT3005,ZOT3014,ZOT3076,ZOT3106,ZOT3107,ZOT3108,ZOT3109,ZOT3135,ZOT3136,ZOT3137,ZOT3152,ZOT3153,ZOT3154,ZOT6006,ZOT6007,ZOT61396,ZOT9043,ZOT9044}
#attribute vred_rab numeric
#data
'2013/01/03 00:00:00.000',PHUG005,4255.3
'2013/01/03 00:00:00.000',PJ7519300,17708.2
'2013/01/03 00:00:00.000',PNT001,13780.7
'2013/01/09 00:00:00.000',GEP61504,1117.8
'2013/01/09 00:00:00.000',TEFS11E27,341.6
'2013/01/10 00:00:00.000',280001,-9000.7
'2013/01/10 00:00:00.000',280005,-2663

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So I tried below query,
evaluate SUMMARIZECOLUMNS('Campaign Summary Customer pool'[CALLPLACEDTIMEUTC],'Campaign Summary Customer pool'[AGENTID],'Campaign Summary Customer pool'[DIALER_SKILL],
FILTER(VALUES('Campaign Summary Customer pool'[CALLPLACEDTIMEUTC]),FORMAT('Campaign Summary Customer pool'[CALLPLACEDTIMEUTC],"dd/mm/yyyy") >= format(value(date(2020,09,08)),"dd/mm/yyyy")
&& format('Campaign Summary Customer pool'[CALLPLACEDTIMEUTC],"dd/mm/yyyy")< format(value(date(2020,09,09)),"dd/mm/yyyy")),
"AGENT CALLS",COUNT('Campaign Summary Customer pool'[I3_IDENTITY]))
This runs fine & but giving output with all date ranges available. Date filter is not working. Any suggesions/Alternative ways to extract date from the datetime field & apply filter on it?

Please check if you get properly formated date from your model
at first:
evaluate
row("x",format(value(date(2020,09,20)),"dd/mm/yyyy"),"Y",format(DATEVALUE("1/16/2020 10:11:42 AM"),"dd/mm/yyyy"))
Second:
evaulate
summarizecolumns(
'Campaign Summary Customer pool'[CALLPLACEDTIMEUTC],
FORMAT('Campaign Summary Customer pool'[CALLPLACEDTIMEUTC],"dd/mm/yyyy")
)
Maybe this will show you where the problem is.

Create table comparing Two Queries with identical fields/field names

I would like to create a table to compare Month To Date (MTD) Vs YTD Sales data metrics
I am using two queries:
1st query = YTD Sales data (Two fields: Sales and Quotes)
2nd query = MTD Sales Data (Two Fields: Sales and Quotes).
Each query has the same field names, just different data
I would like to output a table like the following
How to I create the above table? At the moment I can only create a table like the following:
The latter 1x4 table only works if I appropriately name the fields. But definitely isn’t what I want, because with enough fields, the table could go on forever.
Any help would be appreciated

In the query editor, create a label column for each table that labels what they are and then append the two tables together so you get something like this:
Then you can create a matrix visual with the Label column in the Columns field and the Sales and Quantity columns in the Values area.
Make sure you've switched "Show on Rows" to "On" under the Format > Values section of the Visualizations pane.

WEKA - Compute an average number of non-missing attributes per class attribute

I'm working with a dataset with a lot of missing values. For analytical purposes, I want to be able to compute the average number of non-missing attributes assigned to each class label in the following manner.
Having data like
#relation class
#attribute one {1,2}
#attribute two {1,2}
#attribute three {1,2}
#attribute class {human, animal}
#data
1,Na,Na,human
1,1,Na,human
Na,Na,2,animal
I want to be able to obtain a result like this.
Average non-missing attributes per class label:
- human = 1.5
- animal = 1
Is there any way of doing this in the WEKA explorer?