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demo/GT_M3parallelDB.R
In Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications
# Test SGT - M3 data
# We will use all cores on the server - the more of them the faster the whole process will be.
# If US_DB is true, the forecasts and actuals are saved in an database (SQL Server in this case, but with small changes any ODBC- compatible database, e.g. MySql would do).
library(Mcomp)
library(Rlgt)
library(doParallel)
library(ggplot2)
USE_DB=TRUE #if using the database,
# 1) create the destination tables first (look at the end of the file for an example script)
# 2) create ODBC 64-bit system name "slawek" pointing to the destination database.
if (USE_DB) {
library(RODBC)
}
#set.seed(12)
ODBC_SOURCE_NAME="slawek"
options(width=180)
imageWidth=1000; imageHeight=400
runName='mult_HW_s' #if using a database, make it different for every run
variable='MONTHLY' #or "YEARLY", "QUARTERLY", "MONTHLY" or "OTHER".
LBack=0
OUTPUT_DIR="GT_M3_"
fullOutputDir=file.path(getwd(), paste0(OUTPUT_DIR,variable,'_',runName))
print(paste("The output will go to",fullOutputDir))
if (!file.exists(fullOutputDir)){
dir.create(fullOutputDir)
}
htmlFilePath=file.path(fullOutputDir,paste0("M3_",variable,".html"))
unlink(htmlFilePath)
imagesDir=file.path(fullOutputDir,'images')
if (!file.exists(imagesDir)){
dir.create(imagesDir)
}
cat(paste0("<html><head><title>",OUTPUT_DIR, "</title></head> <body bgcolor=#D0D0D0>"), file = htmlFilePath, append = FALSE)
numOfCores=parallel:::detectCores()
CLUSTER_SIZE=as.integer(numOfCores/4) #Each cluster will use 4 cores (as per default in rlgt.control())
M3.data <- subset(M3,variable)
NUM_OF_CASES=length(M3.data)
#NUM_OF_CASES=20
H=length(M3.data[[1]]$xx)
seasonality=frequency(M3.data[[1]]$x)
startParamToDisplay=2
if (seasonality>1) {
startParamToDisplay=3
}
quantileLoss<-function(forec, actual, tau) {
diff=actual-forec
pinBallL=pmax(diff*tau,diff*(tau-1))
mean(pinBallL/actual)*200
}
legend_str_vect=NULL; legend_cols_vect=NULL; legend_char_vect=NULL
legend_str_vect=c(legend_str_vect,"forecast")
legend_cols_vect=c(legend_cols_vect, 'blue')
legend_char_vect=c(legend_char_vect,'-')
legend_str_vect=c(legend_str_vect,"actuals") #used for short displays
legend_cols_vect=c(legend_cols_vect, 'black')
legend_char_vect=c(legend_char_vect,'-')
#stopCluster(cl) # this is useful if rerunning the script in the same session. First time it will report error - it is OK
#Sys.sleep(2)
unlink("rclust.out")
cl = makeCluster(CLUSTER_SIZE, outfile="rclust.out")
registerDoParallel(cl)
sink(); graphics.off();
stanOutputPath='stanOutput.txt' #in working directory
unlink(stanOutputPath)
now=Sys.time()
if (USE_DB) { #check in meta table if this is the continuation run
#fchannel<- odbcConnect(ODBC_SOURCE_NAME)
fchannel= odbcConnect(ODBC_SOURCE_NAME, uid='SA',pwd='your_pwd')
odbcGetInfo(fchannel)
doneAlready_query=paste0("select dateTimeOfPrediction from M4StanModels",
" where run='",runName,
"' and LBack=",LBack,
" and variable='",variable,
"' ")
doneAlready_df=sqlQuery(fchannel,doneAlready_query, stringsAsFactors =F)
if (nrow(doneAlready_df)>0) {
now=doneAlready_df$dateTimeOfPrediction
} else {
save_df=data.frame(run=runName, LBack=LBack, variable=variable, dateTimeOfPrediction=now, comments='')
sqlSave(fchannel, save_df, tablename='MStanModels', append=T, rownames=F,verbose=F)
}
}
firstTime=TRUE; i=1
ret_df=foreach(i=1:NUM_OF_CASES, .combine=rbind, .inorder=FALSE, .packages=c("Rlgt","RODBC")) %dopar% { # the long loop :-)
if (firstTime==TRUE) { #needs to be done inside every new slave process
#fchannel<- odbcConnect(ODBC_SOURCE_NAME)
fchannel= odbcConnect(ODBC_SOURCE_NAME, uid='SA',pwd='your_pwd')
odbcGetInfo(fchannel)
sink(file=stanOutputPath, append=TRUE, split=TRUE)
firstTime=FALSE
}
series=M3.data[[i]]$sn
actuals <- M3.data[[i]]$xx #need to be here, whethere we calculate or just read from db
proceed=TRUE
if (USE_DB) {
doneAlready_query=paste0("select * from M4StanModels m, M4Stan d",
" where run='",runName,
"' and LBack=",LBack,
" and variable='",variable,
"' and series='",series,
"' and m.dateTimeOfPrediction=d.dateTimeOfPrediction
order by horizon")
doneAlready_df=sqlQuery(fchannel,doneAlready_query, stringsAsFactors =F)
if (nrow(doneAlready_df)>0) {
proceed=FALSE
}
}
if (proceed) {
trainData <- M3.data[[i]]$x
rstanmodel <- rlgt(trainData,
#seasonality.type="generalized", #<--------------------------------------------------------------------------------------------
#level.method="seasAvg", #c("HW", "seasAvg","HW_sAvg"),
control=rlgt.control(NUM_OF_ITER=5000), #we do not need to specify seasonality, as it is extracted from M3.data[[i]]$x
verbose=TRUE)
forec= forecast(rstanmodel, h = H, level=c(90,95,98))
#str(forec, max.level=1)
#forec$lower #5% 2.5% 1%
#forec$upper #95% 97.5% 99%
#str(forec$model$params, max.level=1)
imageFileName=paste(series,'.png',sep='')
relPath=file.path('images',imageFileName)
filePath=file.path(imagesDir, imageFileName)
unlink(filePath)
png(file=filePath, bg="white", width = imageWidth, height = imageHeight, pointsize=16)
plot(forec, main=series)
if (inherits(trainData,"ts")) {
lines(actuals, col=1, lwd=2)
} else {
xs=seq(from=length(trainData)+1,to=length(trainData)+ length(actuals))
lines(xs,actuals, col=1, type='b',lwd=2)
}
legend("topleft", legend_str_vect,
pch=legend_char_vect,
col=legend_cols_vect, cex=1)
dev.off() ;
cat(paste0('<img src="',relPath,'" alt="',series,'" height="',imageHeight,'" width="',imageWidth,'">'), file = htmlFilePath, append = TRUE)
sMAPE=mean(abs(forec$mean-actuals)/(forec$mean+actuals))*200
q05Loss=quantileLoss(forec$lower[,1], actuals, 0.05)
q95Loss=quantileLoss(forec$upper[,1], actuals, 0.95)
q025Loss=quantileLoss(forec$lower[,2], actuals, 0.025)
q975Loss=quantileLoss(forec$upper[,2], actuals, 0.975)
q01Loss=quantileLoss(forec$lower[,3], actuals, 0.01)
q99Loss=quantileLoss(forec$upper[,3], actuals, 0.99)
cat(paste0("<p> ",Sys.time(), " ",series, " sMAPE=",signif(sMAPE,3),"% </p>"), file = htmlFilePath, append = TRUE)
params_txt=NULL;ipar=3
for (ipar in startParamToDisplay:length(forec$model$params)) {
paramName=names(forec$model$params)[ipar]
if (!is.null(params_txt)) params_txt=paste0(params_txt,", ")
params_txt=paste0(params_txt, paramName, ":",signif(median(forec$model$params[[paramName]]),2))
}
cat(paste0("<p> ",params_txt,"</p>"), file = htmlFilePath, append = TRUE)
if (USE_DB) {
save_df=data.frame(dateTimeOfPrediction=now, series=series, horizon=1:H,
actual=actuals, predQ50=forec$mean,
predQ5=forec$lower[,1], predQ95=forec$upper[,1],
predQ2_5=forec$lower[,2], predQ97_5=forec$upper[,2],
predQ1=forec$lower[,3], predQ99=forec$upper[,3])
sqlSave(fchannel, save_df, tablename='M4Stan', append=T, rownames=F,verbose=F)
}
data.frame(series=series, sMAPE=sMAPE,
q05Loss=q05Loss, q95Loss=q95Loss,
q025Loss=q025Loss, q975Loss=q975Loss,
q01Loss=q01Loss, q99Loss=q99Loss,
numOfCases05pExceeded=sum(actuals<forec$lower[,1]),
numOfCases95pExceeded=sum(actuals>forec$upper[,1]),
numOfCases025pExceeded=sum(actuals<forec$lower[,2]),
numOfCases975pExceeded=sum(actuals>forec$upper[,2]),
numOfCases01pExceeded=sum(actuals<forec$lower[,3]),
numOfCases99pExceeded=sum(actuals>forec$upper[,3]))
} else {
#doneAlready_df
if (sum(abs(actuals-doneAlready_df$actual))>1e-4*mean(actuals)) {
stop(paste0("error, diffs between db and new actuals for ",series))
}
sMAPE=mean(abs(doneAlready_df$predQ50-actuals)/(doneAlready_df$predQ50+actuals))*200
q05Loss=quantileLoss(doneAlready_df$predQ5, actuals, 0.05)
q95Loss=quantileLoss(doneAlready_df$predQ95, actuals, 0.95)
q025Loss=quantileLoss(doneAlready_df$predQ2_5, actuals, 0.025)
q975Loss=quantileLoss(doneAlready_df$predQ97_5, actuals, 0.975)
q01Loss=quantileLoss(doneAlready_df$predQ1, actuals, 0.01)
q99Loss=quantileLoss(doneAlready_df$predQ99, actuals, 0.99)
data.frame(series=series, sMAPE=sMAPE,
q05Loss=q05Loss, q95Loss=q95Loss,
q025Loss=q025Loss, q975Loss=q975Loss,
q01Loss=q01Loss, q99Loss=q99Loss,
numOfCases05pExceeded=sum(actuals<doneAlready_df$predQ5),
numOfCases95pExceeded=sum(actuals>doneAlready_df$predQ95),
numOfCases025pExceeded=sum(actuals<doneAlready_df$predQ2_5),
numOfCases975pExceeded=sum(actuals>doneAlready_df$predQ97_5),
numOfCases01pExceeded=sum(actuals<doneAlready_df$predQ1),
numOfCases99pExceeded=sum(actuals>doneAlready_df$predQ99)
)
} #done already
} #through cases
sMAPE=mean(ret_df$sMAPE)
q05Loss=mean(ret_df$q05Loss)
q95Loss=mean(ret_df$q95Loss)
q025Loss=mean(ret_df$q025Loss)
q975Loss=mean(ret_df$q975Loss)
q01Loss=mean(ret_df$q01Loss)
q99Loss=mean(ret_df$q99Loss)
exceed05=mean(ret_df$numOfCases05pExceeded)/H*100
exceed95=mean(ret_df$numOfCases95pExceeded)/H*100
exceed025=mean(ret_df$numOfCases025pExceeded)/H*100
exceed975=mean(ret_df$numOfCases975pExceeded)/H*100
exceed01=mean(ret_df$numOfCases01pExceeded)/H*100
exceed99=mean(ret_df$numOfCases99pExceeded)/H*100
print(paste0("SUMMARY: Num of cases:", nrow(ret_df), ", sMAPE:",signif(sMAPE,4),
', % of time exceeded 1p:',signif(exceed01,4),
', 2.5p:',signif(exceed025,4),
', 5p:',signif(exceed05,4),
', 95p:',signif(exceed95,4),
', 97.5p:',signif(exceed975,4),
', 99p:',signif(exceed99,4),
', qLoss 1p:',signif(q01Loss,4),
', 2.5p:',signif(q025Loss,4),
', 5p:',signif(q05Loss,4),
', 95p:',signif(q95Loss,4),
', 97.5p:',signif(q975Loss,4),
', 99p:',signif(q99Loss,4)
))
#CREATE TABLE [dbo].[MStanModels](
# [run] [varchar](164) NOT NULL,
# [LBack] [tinyint] NOT NULL,
# [variable] [varchar](20) NOT NULL,
# [dateTimeOfPrediction] [datetime] NOT NULL,
# [comments] [varchar](300) NULL,
# CONSTRAINT [MStanModels_pk] PRIMARY KEY CLUSTERED
# (
# [run] ASC,
# [LBack] ASC,
# [variable] ASC,
# [dateTimeOfPrediction] asc
# )
#)
#CREATE TABLE [dbo].[MStan](
#[dateTimeOfPrediction] [datetime] NOT NULL,
#[series] [varchar](50) NOT NULL,
#[horizon] [tinyint] NOT NULL,
#[actual] [real] NOT NULL,
#[predQ50] [real] NOT NULL,
#[predQ5] [real] NOT NULL,
#[predQ95] [real] NOT NULL,
#[predQ2_5] [real] NOT NULL,
#[predQ97_5] [real] NOT NULL,
#[predQ1] [real] NOT NULL,
#[predQ99] [real] NOT NULL,
#CONSTRAINT [Mpy_PK] PRIMARY KEY CLUSTERED
#(
# [dateTimeOfPrediction] ASC,
# [series] ASC,
# [horizon] ASC
#))
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# Test SGT - M3 data
# We will use all cores on the server - the more of them the faster the whole process will be.
# If US_DB is true, the forecasts and actuals are saved in an database (SQL Server in this case, but with small changes any ODBC- compatible database, e.g. MySql would do).
library(Mcomp)
library(Rlgt)
library(doParallel)
library(ggplot2)
USE_DB=TRUE #if using the database,
# 1) create the destination tables first (look at the end of the file for an example script)
# 2) create ODBC 64-bit system name "slawek" pointing to the destination database.
if (USE_DB) {
library(RODBC)
}
#set.seed(12)
ODBC_SOURCE_NAME="slawek"
options(width=180)
imageWidth=1000; imageHeight=400
runName='mult_HW_s' #if using a database, make it different for every run
variable='MONTHLY' #or "YEARLY", "QUARTERLY", "MONTHLY" or "OTHER".
LBack=0
OUTPUT_DIR="GT_M3_"
fullOutputDir=file.path(getwd(), paste0(OUTPUT_DIR,variable,'_',runName))
print(paste("The output will go to",fullOutputDir))
if (!file.exists(fullOutputDir)){
dir.create(fullOutputDir)
}
htmlFilePath=file.path(fullOutputDir,paste0("M3_",variable,".html"))
unlink(htmlFilePath)
imagesDir=file.path(fullOutputDir,'images')
if (!file.exists(imagesDir)){
dir.create(imagesDir)
}
cat(paste0("<html><head><title>",OUTPUT_DIR, "</title></head> <body bgcolor=#D0D0D0>"), file = htmlFilePath, append = FALSE)
numOfCores=parallel:::detectCores()
CLUSTER_SIZE=as.integer(numOfCores/4) #Each cluster will use 4 cores (as per default in rlgt.control())
M3.data <- subset(M3,variable)
NUM_OF_CASES=length(M3.data)
#NUM_OF_CASES=20
H=length(M3.data[[1]]$xx)
seasonality=frequency(M3.data[[1]]$x)
startParamToDisplay=2
if (seasonality>1) {
startParamToDisplay=3
}
quantileLoss<-function(forec, actual, tau) {
diff=actual-forec
pinBallL=pmax(diff*tau,diff*(tau-1))
mean(pinBallL/actual)*200
}
legend_str_vect=NULL; legend_cols_vect=NULL; legend_char_vect=NULL
legend_str_vect=c(legend_str_vect,"forecast")
legend_cols_vect=c(legend_cols_vect, 'blue')
legend_char_vect=c(legend_char_vect,'-')
legend_str_vect=c(legend_str_vect,"actuals") #used for short displays
legend_cols_vect=c(legend_cols_vect, 'black')
legend_char_vect=c(legend_char_vect,'-')
#stopCluster(cl) # this is useful if rerunning the script in the same session. First time it will report error - it is OK
#Sys.sleep(2)
unlink("rclust.out")
cl = makeCluster(CLUSTER_SIZE, outfile="rclust.out")
registerDoParallel(cl)
sink(); graphics.off();
stanOutputPath='stanOutput.txt' #in working directory
unlink(stanOutputPath)
now=Sys.time()
if (USE_DB) { #check in meta table if this is the continuation run
#fchannel<- odbcConnect(ODBC_SOURCE_NAME)
fchannel= odbcConnect(ODBC_SOURCE_NAME, uid='SA',pwd='your_pwd')
odbcGetInfo(fchannel)
doneAlready_query=paste0("select dateTimeOfPrediction from M4StanModels",
" where run='",runName,
"' and LBack=",LBack,
" and variable='",variable,
"' ")
doneAlready_df=sqlQuery(fchannel,doneAlready_query, stringsAsFactors =F)
if (nrow(doneAlready_df)>0) {
now=doneAlready_df$dateTimeOfPrediction
} else {
save_df=data.frame(run=runName, LBack=LBack, variable=variable, dateTimeOfPrediction=now, comments='')
sqlSave(fchannel, save_df, tablename='MStanModels', append=T, rownames=F,verbose=F)
}
}
firstTime=TRUE; i=1
ret_df=foreach(i=1:NUM_OF_CASES, .combine=rbind, .inorder=FALSE, .packages=c("Rlgt","RODBC")) %dopar% { # the long loop :-)
if (firstTime==TRUE) { #needs to be done inside every new slave process
#fchannel<- odbcConnect(ODBC_SOURCE_NAME)
fchannel= odbcConnect(ODBC_SOURCE_NAME, uid='SA',pwd='your_pwd')
odbcGetInfo(fchannel)
sink(file=stanOutputPath, append=TRUE, split=TRUE)
firstTime=FALSE
}
series=M3.data[[i]]$sn
actuals <- M3.data[[i]]$xx #need to be here, whethere we calculate or just read from db
proceed=TRUE
if (USE_DB) {
doneAlready_query=paste0("select * from M4StanModels m, M4Stan d",
" where run='",runName,
"' and LBack=",LBack,
" and variable='",variable,
"' and series='",series,
"' and m.dateTimeOfPrediction=d.dateTimeOfPrediction
order by horizon")
doneAlready_df=sqlQuery(fchannel,doneAlready_query, stringsAsFactors =F)
if (nrow(doneAlready_df)>0) {
proceed=FALSE
}
}
if (proceed) {
trainData <- M3.data[[i]]$x
rstanmodel <- rlgt(trainData,
#seasonality.type="generalized", #<--------------------------------------------------------------------------------------------
#level.method="seasAvg", #c("HW", "seasAvg","HW_sAvg"),
control=rlgt.control(NUM_OF_ITER=5000), #we do not need to specify seasonality, as it is extracted from M3.data[[i]]$x
verbose=TRUE)
forec= forecast(rstanmodel, h = H, level=c(90,95,98))
#str(forec, max.level=1)
#forec$lower #5% 2.5% 1%
#forec$upper #95% 97.5% 99%
#str(forec$model$params, max.level=1)
imageFileName=paste(series,'.png',sep='')
relPath=file.path('images',imageFileName)
filePath=file.path(imagesDir, imageFileName)
unlink(filePath)
png(file=filePath, bg="white", width = imageWidth, height = imageHeight, pointsize=16)
plot(forec, main=series)
if (inherits(trainData,"ts")) {
lines(actuals, col=1, lwd=2)
} else {
xs=seq(from=length(trainData)+1,to=length(trainData)+ length(actuals))
lines(xs,actuals, col=1, type='b',lwd=2)
}
legend("topleft", legend_str_vect,
pch=legend_char_vect,
col=legend_cols_vect, cex=1)
dev.off() ;
cat(paste0('<img src="',relPath,'" alt="',series,'" height="',imageHeight,'" width="',imageWidth,'">'), file = htmlFilePath, append = TRUE)
sMAPE=mean(abs(forec$mean-actuals)/(forec$mean+actuals))*200
q05Loss=quantileLoss(forec$lower[,1], actuals, 0.05)
q95Loss=quantileLoss(forec$upper[,1], actuals, 0.95)
q025Loss=quantileLoss(forec$lower[,2], actuals, 0.025)
q975Loss=quantileLoss(forec$upper[,2], actuals, 0.975)
q01Loss=quantileLoss(forec$lower[,3], actuals, 0.01)
q99Loss=quantileLoss(forec$upper[,3], actuals, 0.99)
cat(paste0("<p> ",Sys.time(), " ",series, " sMAPE=",signif(sMAPE,3),"% </p>"), file = htmlFilePath, append = TRUE)
params_txt=NULL;ipar=3
for (ipar in startParamToDisplay:length(forec$model$params)) {
paramName=names(forec$model$params)[ipar]
if (!is.null(params_txt)) params_txt=paste0(params_txt,", ")
params_txt=paste0(params_txt, paramName, ":",signif(median(forec$model$params[[paramName]]),2))
}
cat(paste0("<p> ",params_txt,"</p>"), file = htmlFilePath, append = TRUE)
if (USE_DB) {
save_df=data.frame(dateTimeOfPrediction=now, series=series, horizon=1:H,
actual=actuals, predQ50=forec$mean,
predQ5=forec$lower[,1], predQ95=forec$upper[,1],
predQ2_5=forec$lower[,2], predQ97_5=forec$upper[,2],
predQ1=forec$lower[,3], predQ99=forec$upper[,3])
sqlSave(fchannel, save_df, tablename='M4Stan', append=T, rownames=F,verbose=F)
}
data.frame(series=series, sMAPE=sMAPE,
q05Loss=q05Loss, q95Loss=q95Loss,
q025Loss=q025Loss, q975Loss=q975Loss,
q01Loss=q01Loss, q99Loss=q99Loss,
numOfCases05pExceeded=sum(actuals<forec$lower[,1]),
numOfCases95pExceeded=sum(actuals>forec$upper[,1]),
numOfCases025pExceeded=sum(actuals<forec$lower[,2]),
numOfCases975pExceeded=sum(actuals>forec$upper[,2]),
numOfCases01pExceeded=sum(actuals<forec$lower[,3]),
numOfCases99pExceeded=sum(actuals>forec$upper[,3]))
} else {
#doneAlready_df
if (sum(abs(actuals-doneAlready_df$actual))>1e-4*mean(actuals)) {
stop(paste0("error, diffs between db and new actuals for ",series))
}
sMAPE=mean(abs(doneAlready_df$predQ50-actuals)/(doneAlready_df$predQ50+actuals))*200
q05Loss=quantileLoss(doneAlready_df$predQ5, actuals, 0.05)
q95Loss=quantileLoss(doneAlready_df$predQ95, actuals, 0.95)
q025Loss=quantileLoss(doneAlready_df$predQ2_5, actuals, 0.025)
q975Loss=quantileLoss(doneAlready_df$predQ97_5, actuals, 0.975)
q01Loss=quantileLoss(doneAlready_df$predQ1, actuals, 0.01)
q99Loss=quantileLoss(doneAlready_df$predQ99, actuals, 0.99)
data.frame(series=series, sMAPE=sMAPE,
q05Loss=q05Loss, q95Loss=q95Loss,
q025Loss=q025Loss, q975Loss=q975Loss,
q01Loss=q01Loss, q99Loss=q99Loss,
numOfCases05pExceeded=sum(actuals<doneAlready_df$predQ5),
numOfCases95pExceeded=sum(actuals>doneAlready_df$predQ95),
numOfCases025pExceeded=sum(actuals<doneAlready_df$predQ2_5),
numOfCases975pExceeded=sum(actuals>doneAlready_df$predQ97_5),
numOfCases01pExceeded=sum(actuals<doneAlready_df$predQ1),
numOfCases99pExceeded=sum(actuals>doneAlready_df$predQ99)
)
} #done already
} #through cases
sMAPE=mean(ret_df$sMAPE)
q05Loss=mean(ret_df$q05Loss)
q95Loss=mean(ret_df$q95Loss)
q025Loss=mean(ret_df$q025Loss)
q975Loss=mean(ret_df$q975Loss)
q01Loss=mean(ret_df$q01Loss)
q99Loss=mean(ret_df$q99Loss)
exceed05=mean(ret_df$numOfCases05pExceeded)/H*100
exceed95=mean(ret_df$numOfCases95pExceeded)/H*100
exceed025=mean(ret_df$numOfCases025pExceeded)/H*100
exceed975=mean(ret_df$numOfCases975pExceeded)/H*100
exceed01=mean(ret_df$numOfCases01pExceeded)/H*100
exceed99=mean(ret_df$numOfCases99pExceeded)/H*100
print(paste0("SUMMARY: Num of cases:", nrow(ret_df), ", sMAPE:",signif(sMAPE,4),
', % of time exceeded 1p:',signif(exceed01,4),
', 2.5p:',signif(exceed025,4),
', 5p:',signif(exceed05,4),
', 95p:',signif(exceed95,4),
', 97.5p:',signif(exceed975,4),
', 99p:',signif(exceed99,4),
', qLoss 1p:',signif(q01Loss,4),
', 2.5p:',signif(q025Loss,4),
', 5p:',signif(q05Loss,4),
', 95p:',signif(q95Loss,4),
', 97.5p:',signif(q975Loss,4),
', 99p:',signif(q99Loss,4)
))
#CREATE TABLE [dbo].[MStanModels](
# [run] [varchar](164) NOT NULL,
# [LBack] [tinyint] NOT NULL,
# [variable] [varchar](20) NOT NULL,
# [dateTimeOfPrediction] [datetime] NOT NULL,
# [comments] [varchar](300) NULL,
# CONSTRAINT [MStanModels_pk] PRIMARY KEY CLUSTERED
# (
# [run] ASC,
# [LBack] ASC,
# [variable] ASC,
# [dateTimeOfPrediction] asc
# )
#)
#CREATE TABLE [dbo].[MStan](
#[dateTimeOfPrediction] [datetime] NOT NULL,
#[series] [varchar](50) NOT NULL,
#[horizon] [tinyint] NOT NULL,
#[actual] [real] NOT NULL,
#[predQ50] [real] NOT NULL,
#[predQ5] [real] NOT NULL,
#[predQ95] [real] NOT NULL,
#[predQ2_5] [real] NOT NULL,
#[predQ97_5] [real] NOT NULL,
#[predQ1] [real] NOT NULL,
#[predQ99] [real] NOT NULL,
#CONSTRAINT [Mpy_PK] PRIMARY KEY CLUSTERED
#(
# [dateTimeOfPrediction] ASC,
# [series] ASC,
# [horizon] ASC
#))
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