Future prediction with easy VAR model.
Use the vector autoregressive(VAR) model to predict future values from time series data. In this Shiny application, the differences of time series data are taken and applied to the VAR model to predict the future value by predicting the future difference.
First, access CRAN, download R and install. You can install from R console. If shiny, zoo and vars are not installed on your PC, install shiny, zoo and vars with Internet connection.
install.packages(c("shiny", "zoo", "vars"))
If shiny, zoo and vars are installed, it can be started from R console with the following command.
shiny::runGitHub("ezvar", "ToshihiroIguchi")
Host the Shiny application from GitHub in a private network. Enter the following command in R console.
#Port specification
port <- 1234
#Acquire private address information
ipconfig.dat <- system("ipconfig", intern = TRUE)
ipv4.dat <- ipconfig.dat[grep("IPv4", ipconfig.dat)][1]
ip <- gsub(".*? ([[:digit:]])", "\\1", ipv4.dat)
#Host the Shiny application from GitHub
shiny::runGitHub("ezvar", "ToshihiroIguchi", launch.browser = FALSE, port = port, host = ip)
If you are in the private network, you can also launch the Shiny application by entering the URL following Listing on to a browser.
Operate with the browser.
Select csv file in Browse....
The contents of the csv file are displayed on the Data tab.
Analyze only numeric data. It is not analyzed if it is recognized as an application character. Be careful as commas are recognized as characters.
Enter the integer value of The highest lag order.
For example, when predicting a numerical value every month, it can be expected to be a 12-month cycle, so you would enter a value of 12 or more.
Enter the integer value of Period.
Because it is difficult to predict long-term values, small numbers are preferable.
MIT License
Copyright (c) 2018 Toshihiro Iguchi
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Toshihiro Iguchi