README.md

In TheRealSvc/forecast4you: a univariate forecasting wrapper

ForecastFramework

A simple framework to perform univariate forecasting in R

The idea is to generate a simple, yet scaleable framework to register custom univariate forecasting algorithms to keep the "main_script" clean. This is achieved by working with a "function factory pattern" to generate all relevant functions (preproc, outlier, fitting and prediction ...) functions in a list. Like this a highly "function-parallel" code can be written (as opposed to data-parallel). Hence this pattern might be valuable for small-data/big-compute scenarious.

Currently the software architecture is not final and the idea doesnt shine through nicely. Future work will get it there - hopefully :-)

example and demo

```{r class.source="bg-danger", class.output="bg-warning"}

main workflow script ... just execute it :-)

source("R/helper_funs_framework.R")

library(forecast4you)

library(forecast)

--------- 1 Parametrization --------------------------------

TrainLength <- 48 # length of the training data for the splits ValidLength <- 6 # length of Validation duration for backtesting

parametrize models --> this will go as argument into the fitting function ... this is up to you what to define here !

new models must be added in the FunGen function in the helper_funs_framework

sigLevels <- c(80,95) modelParams <- list("lastval"=list(type="lastval", sigLevels=sigLevels), "LinReg"=list(type="linreg", numobs=3, sigLevels=sigLevels), "ets no trend season no damp"=list(type="ets",model="ANA", damped=FALSE, sigLevels=sigLevels), "Arima (1,0,0) with drift"= list(type="arima", order=c(1,0,0), include.drift=1, include.mean=1, sigLevels=sigLevels), "percentage"=list(type="percentage",bandPM=10, sigLevels=sigLevels))

---------------------------------------------------------

---- 2 generate some testdata in the required data representation --------

dat <- as.data.frame(matrix(runif(600,-10,10), ncol=10, nrow=60))

randomFun <- function(x) { a = runif(1,-1,1) b = runif(1,-10,10) sinPeriodicity = runif(1,0,180) sinAmp = runif(1,10,50) return( x + aseq(1,length(x),1) +b + sin(2piseq(1,length(x),1)/sinPeriodicity)sinAmp ) }

for(i in 1:ncol(dat)) { dat[,i] <- randomFun(dat[,i]) }

colnames(dat) <- c("name" , paste0("timeseriesNo_",1:(ncol(dat)-1))) # name dat[,1] <- paste0("t_",1:nrow(dat))

-------------------------------------------------------------------------

------ 3 creation of the models used for fitting to the data --------------

modelFitFuns <- CreateModelFuns(paramsList = modelParams) # output is a list of functions by using a "function factory pattern" (no calculation happens here)

creation of data splits (train and Validation) and use a list tree as data representation

Note: as many train/valid sets will be generated as possible given the data, TrainLength and ValidLength. Its all stored in a list tree for further enrichment

splitFun <- CreateWalkForwardFuns(lTrain=TrainLength, lValid=ValidLength) splitDat <- lapply(dat[,2:ncol(dat)], splitFun)

actual prediction: calling all the fitting functions on all the splits of all the timeseries.

Note: this can be easily parallelized (though it is not yet in that shape)

splitDat2 <- DoPrediction(splitDat=splitDat, modelFitFuns, ValiLength = ValidLength) # model fitting and forecast as a batch job

---- 3 some plotting --------

just play around. A wrapper around this could make sense in order to show more info in one plot

graphics.off() plotSingleModelSingleAccount(accountName = "timeseriesNo_1", splitDat = splitDat2, modelName = "ets no trend season no damp", split=1)

```

TheRealSvc/forecast4you documentation built on Dec. 18, 2021, 4:10 p.m.