https://github.com/thiyangt/fformpp

FFORMPP: Feature-based FORecast Model Performance Prediction
https://github.com/thiyangt/fformpp

Last synced: 8 days ago
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FFORMPP: Feature-based FORecast Model Performance Prediction

• Host: GitHub
• URL: https://github.com/thiyangt/fformpp
• Owner: thiyangt
• Created: 2019-02-18T03:42:52.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-12-21T10:01:36.000Z (11 months ago)
• Last Synced: 2024-03-06T01:49:47.096Z (8 months ago)
• Language: R
• Homepage:
• Size: 1.54 MB
• Stars: 9
• Watchers: 6
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

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README

        
---

output: github_document

---

[![Project Status: Active ? The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) [![Build Status](https://travis-ci.org/thiyangt/fformpp.svg?branch=master)](https://travis-ci.org/thiyangt/fformpp.svg?branch=masterr)

[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# fformpp 

## Installation

The linked packages [flutils](https://github.com/feng-li/flutils) and [movingknots](https://github.com/feng-li/movingknots) should be installed prior to the installation of fformpp.

```r

# install.packages("devtools")

devtools::install_github("thiyangt/fformpp")

library(fformpp)

```

## Usage

### Following example illustrates how package functionalities work

**Load packages**

```{r}

library(methods)

library(MASS)

library(Matrix)

library(mvtnorm)

library(flutils)

library(fformpp)

library(seer)

library(parallel) #getDefaultCluster

```

**Load example dataset**

```r

data(features.df)

data(forecast.error)

features_mat <- as.matrix(features.df)

forecast.error <- as.matrix(forecast.error)

```

**Fit surface regression model**

```r

## This will take time. This model is saved in the package. The fitted model is  saved into the package for easy references.

n <- dim(forecast.error)[1]

p <- dim(forecast.error)[2]

fformpp.model <- fit_fformpp(feamat=features_mat, accmat=forecast.error, 

                             sknots=2, aknots=2,

                            fix.s=0, fix.a=0, fix.shrinkage=1:p,            fix.covariance=0,

                            fix.coefficients=0, n.iter=100,

                            knot.moving.algorithm="Random-Walk",

                            ptype=c("identity", "identity", "identity"),

                            prior.knots=n)

```

**Predict forecast error on new data**

```{r}

data("fformpp.model")

data("forecast.error.m1")

data("features.df.m1")

predict.m1 <- predict_fformpp(fformpp.model, features.df.m1, c("ets", "arima", "rw", "rwd", "wn", "theta", "nn"), log=FALSE, final.estimate=median)

head(predict.m1)

```

**Generate forecast from the model with minimum forecast error**

```{r}

library(Mcomp)

yearlym1 <- subset(M1, "yearly")

data("fcast_m1")

min.fcasterror <- individual_forecast(predicted=predict.m1, 

                                      accmat=cal_MASE, 

                                      real.error=forecast.error.m1, 

                                      tslist=yearlym1, 

                                      forecast_list = fcast_m1,

                                      h=6)

min.fcasterror

```

**Generate combination forecasts**

```{r}

library(Mcomp)

yearlym1 <- subset(M1, "yearly")

data("fcast_m1")

min.fcasterror.comb <- combination_forecast(predicted=predict.m1[1:2,], 

                                      ncomp=2,

                                      accmat=cal_MASE, 

                                      real.error=forecast.error.m1, 

                                      tslist=yearlym1, 

                                      forecast_list = fcast_m1,

                                      h=6, weights=FALSE, measure="mean")

min.fcasterror.comb

```