Comments (4)
Currently, AutoTS() runs a single series at a time. You should supply a data.table with a date column and a target column (in that order), like the example in the help file. If you want to run through multiple series, create a loop and subset the data before each AutoTS() run.
This works for me:
data <- structure(list(zadnja = c(421, 425, 432, 415, 414, 409.99, 407,
415, 424.99, 432, 425, 433, 428, 428.99, 425, 425, 420, 420,
420, 419.98, 415, 410, 407, 407.5, 399.98, 400.05, 380, 400,
394.99, 389.98, 395.05, 381.5, 385, 395.9, 383, 376, 390, 385.01,
385, 379, 375.1, 380, 378.99, 368.99, 355.75, 367.97, 370, 376,
386.98, 392), index = structure(c(13917, 13920, 13921, 13922,
13923, 13924, 13927, 13928, 13929, 13930, 13931, 13934, 13935,
13936, 13937, 13938, 13941, 13942, 13943, 13944, 13945, 13948,
13949, 13950, 13951, 13952, 13955, 13956, 13957, 13958, 13963,
13964, 13965, 13966, 13969, 13970, 13971, 13972, 13973, 13976,
13977, 13978, 13979, 13980, 13983, 13984, 13985, 13986, 13987,
13990), class = "Date")), row.names = c(NA, -50L), index_quo = ~index, index_time_zone = "UTC", class = c("tbl_time",
"tbl_df", "tbl", "data.frame"))
data <- data.table::as.data.table(data)
data.table::setcolorder(data, c(2,1))
xx <- RemixAutoML::AutoTS(data,
TargetName = "zadnja",
DateName = "index",
FCPeriods = 1,
HoldOutPeriods = 1,
EvaluationMetric = "MAPE",
TimeUnit = "day",
Lags = 1,
SLags = 1,
NumCores = 4,
SkipModels = c("NNET","TBATS","ETS","TSLM","ARFIMA","DSHW"),
StepWise = TRUE,
TSClean = FALSE,
ModelFreq = TRUE,
PrintUpdates = FALSE)
P.S. I keep the code in a single file because it's easier for me to develop that way. I understand it's more challenging to find specific code blocks that way and I'll be splitting them up eventually, when development slows down.
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I would like to add that I added in the functionality to remove all extraneous columns you may put in your data along with ensuring the ordering of columns are correct.
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Ok, it works now.
Thanks.
It would be to incorporate multivariate models in the future.
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You should check out AutoCatBoostCARMA(). It's a multivariate catboost forecasting function. It utilizes calendar, trend, and ARMA variables, and replicates an ARMA forecasting process. I tested it recently on some Walmart store / department data and was able to generate forecasts for 2660 store / dept's in about 15 minutes on GPU.
You need to have your data in long format - that is, you need a date column, values column, and categorical columns such that, by filtering for a unique set of factor levels you will have an individual series. So basically, stacked data.
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Related Issues (20)
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