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A new regression approach for time series modeling. This repo will evolve into an R package in the future.

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sametsoekel/eXtreme-Least-Squares

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XLS

License: GPL-3

An R package that allows modeling with partial weighted regression to remove over effect in time series models that are heavily loaded on the lag variable and to fit the model in a healthy state.

Installation

You can install the released version of XLS from CRAN with:

install.packages("XLS")

Or install the development version of XLS from Github with:

devtools::install_github("sametsoekel/eXtreme-Least-Squares")

Example

Fit an eXtreme Least Squares Model:

library(XLS)

df <- datasets::airquality

ordered_df <- df[with(df,order(Month,Day)),]

model <- xls.fit(Ozone ~ Solar.R + Wind + Temp,na.omit(ordered_df),
error_weights = c(0.4,0.3,0.2,0.1),error_ahead_level = 4)

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A new regression approach for time series modeling. This repo will evolve into an R package in the future.

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