我正在为零售商预测日内交易。我们想知道是否可以根据当天的销售量来预测一天的其余时间。我正在按时间序列处理大约3年的数据，这给了我大约26,000行数据。
我从来没有使用过如此大的时间序列，因此我的方法可能不可行。或可能没有使auto.arima()处理如此大的数据。
我尝试将数据限制为300行，但取得了一定的成功，但是没有发现任何适用于较大数据集的数据。根据我的发现，auto.arima()甚至没有by =的论点。
my_ts <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 3,
3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 4, 1,
1, 0, 3, 1, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4,
1, 9, 1, 6, 5, 1, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 3, 1, 0, 3, 5, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 3, 1, 0, 0, 6, 0, 6, 0, 1, 2, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 4, 6, 5, 0, 1, 0, 2, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 2, 0, 2, 0, 0, 0, 1, 1,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 0, 1, 0,
3, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0,
8, 2, 7, 4, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 6, 2, 0, 1, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -1, 2, 3, 1, 0, 0, 2, 5, 7, 0, -1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 6, 1, 2, 2, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 0, 2, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 0, 0,
2, 2, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2,
2, 0, 2, 3, 6, 5, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 2, 2, 2, 1, 4, 3, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 3, 4, 0, 4, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 3, 2, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 0, 3, 4, 3, 0, 0, 2, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 1, 1, 1, 0, 3,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 2,
0, 1, 1, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1,
1, 4, 1, 2, 9, 1, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 4, 1, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 3, 1, 2, 1, 1, 4, 0, 3, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 2, 1, 6, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 0, 1, 2, 1, 4, 0, 0, 5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 5, 1, 1, 3, 3,
4, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 3, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
3, 0, 2, 8, 0, 2, 3, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 2, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 3, 1, 2, 2, 3, 1, 4, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 4, 1, 2, 0, 0, 5, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 1, 1, 4, 0, 4, 3,
2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6, 1, 0, 0, 0,
3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8,
1, 2, 0, 4, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
1, 6, 7, 0, 1, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 5, 0, 2, 1, 4, 1, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 1, 4, 7, 5, 0, 7, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 6, 0, 4, 6, 2, 1, 3, 5, 3, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 3, 0, 1, 1, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 6, 0, 0), index = structure(c(1435190400,
1435194000, 1435197600, 1435201200, 1435204800, 1435208400, 1435212000,
1435215600, 1435219200, 1435222800, 1435226400, 1435230000, 1435233600,
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1435258800, 1435262400, 1435266000, 1435269600, 1435273200, 1435276800,
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1435302000, 1435305600, 1435309200, 1435312800, 1435316400, 1435320000,
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1435474800, 1435478400, 1435482000, 1435485600, 1435489200, 1435492800,
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1436122800, 1436126400, 1436130000, 1436133600, 1436137200, 1436140800,
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1436166000, 1436169600, 1436173200, 1436176800, 1436180400, 1436184000,
1436187600, 1436191200, 1436194800, 1436198400, 1436202000, 1436205600,
1436209200, 1436212800, 1436216400, 1436220000, 1436223600, 1436227200,
1436230800, 1436234400, 1436238000, 1436241600, 1436245200, 1436248800,
1436252400, 1436256000, 1436259600, 1436263200, 1436266800, 1436270400,
1436274000, 1436277600, 1436281200, 1436284800, 1436288400, 1436292000,
1436295600, 1436299200, 1436302800, 1436306400, 1436310000, 1436313600,
1436317200, 1436320800, 1436324400, 1436328000, 1436331600, 1436335200,
1436338800, 1436342400, 1436346000, 1436349600, 1436353200, 1436356800,
1436360400, 1436364000, 1436367600, 1436371200, 1436374800, 1436378400,
1436382000, 1436385600, 1436389200, 1436392800, 1436396400, 1436400000,
1436403600, 1436407200, 1436410800, 1436414400, 1436418000, 1436421600,
1436425200, 1436428800, 1436432400, 1436436000, 1436439600, 1436443200,
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1436468400, 1436472000, 1436475600, 1436479200, 1436482800, 1436486400,
1436490000, 1436493600, 1436497200, 1436500800, 1436504400, 1436508000,
1436511600, 1436515200, 1436518800, 1436522400, 1436526000, 1436529600,
1436533200, 1436536800, 1436540400, 1436544000, 1436547600, 1436551200,
1436554800, 1436558400, 1436562000, 1436565600, 1436569200, 1436572800,
1436576400, 1436580000, 1436583600, 1436587200, 1436590800, 1436594400,
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1436619600, 1436623200, 1436626800, 1436630400, 1436634000, 1436637600,
1436641200, 1436644800, 1436648400, 1436652000, 1436655600, 1436659200,
1436662800, 1436666400, 1436670000, 1436673600, 1436677200, 1436680800,
1436684400, 1436688000, 1436691600, 1436695200, 1436698800, 1436702400,
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1436878800, 1436882400, 1436886000, 1436889600, 1436893200, 1436896800,
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1437742800, 1437746400, 1437750000, 1437753600, 1437757200, 1437760800,
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1438174800, 1438178400, 1438182000, 1438185600, 1438189200, 1438192800,
1438196400, 1438200000, 1438203600, 1438207200, 1438210800, 1438214400,
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1438239600, 1438243200, 1438246800, 1438250400, 1438254000, 1438257600,
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1438282800, 1438286400, 1438290000, 1438293600, 1438297200, 1438300800,
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1438326000, 1438329600, 1438333200, 1438336800, 1438340400, 1438344000,
1438347600, 1438351200, 1438354800, 1438358400, 1438362000, 1438365600,
1438369200, 1438372800, 1438376400, 1438380000, 1438383600, 1438387200,
1438390800, 1438394400, 1438398000, 1438401600, 1438405200, 1438408800,
1438412400, 1438416000, 1438419600, 1438423200, 1438426800, 1438430400,
1438434000, 1438437600, 1438441200, 1438444800, 1438448400, 1438452000,
1438455600, 1438459200, 1438462800, 1438466400, 1438470000, 1438473600,
1438477200, 1438480800, 1438484400, 1438488000, 1438491600, 1438495200,
1438498800, 1438502400, 1438506000, 1438509600, 1438513200, 1438516800,
1438520400, 1438524000, 1438527600, 1438531200, 1438534800, 1438538400,
1438542000, 1438545600, 1438549200, 1438552800, 1438556400, 1438560000,
1438563600, 1438567200, 1438570800, 1438574400, 1438578000, 1438581600,
1438585200, 1438588800, 1438592400, 1438596000, 1438599600, 1438603200,
1438606800, 1438610400, 1438614000, 1438617600, 1438621200, 1438624800,
1438628400, 1438632000, 1438635600, 1438639200, 1438642800, 1438646400,
1438650000, 1438653600, 1438657200, 1438660800, 1438664400, 1438668000,
1438671600, 1438675200, 1438678800, 1438682400, 1438686000, 1438689600,
1438693200, 1438696800, 1438700400, 1438704000, 1438707600, 1438711200,
1438714800, 1438718400, 1438722000, 1438725600, 1438729200, 1438732800,
1438736400, 1438740000, 1438743600, 1438747200, 1438750800, 1438754400,
1438758000, 1438761600, 1438765200, 1438768800, 1438772400, 1438776000,
1438779600, 1438783200, 1438786800), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), class = c("zooreg", "zoo"), frequency = 24)
fit1 <-auto.arima(my_ts,seasonal = TRUE)
我希望通过Arima来获得模型，但是我只得到错误：
“ seq.default（head（tt，1），tail（tt，1），deltat）中的错误： “按”的论点太小了”