Forecasting-with-R/LinearRegressionForecasting.Rmd at master · Roznn/Forecasting-with-R · GitHub

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---
title: "Forecasting with tslm()"
author: "Rozenn Dahyot  https://Roznn.github.io"
date: "2020"
output:
  rmdformats::readthedown:
    highlight: kate
---


```{r setup, echo=FALSE, cache=FALSE}
library(knitr)
library(rmdformats)

## Global options
options(max.print="75")
opts_chunk$set(echo=TRUE,
	             cache=FALSE,
               prompt=FALSE,
               tidy=TRUE,
               comment=NA,
               message=FALSE,
               warning=FALSE)
opts_knit$set(width=75)
```


# Example with *beer* time series


This example below shows  *lm()* and *tslm()* functions used to fit the beer  time series that has a seasonal pattern and no trend. We note that *tslm()* creates automatically the indicator variables for modelling the seasonal patterns.

The model fitted corresponds to:
$$
\text{beer}_t=\underbrace{\beta_0 + \beta_2 \ \text{Feb}_t+ \cdots+\beta_{12} \ \text{Dec}_t }_{\widehat{\text{beer}}_t}+\epsilon_t
$$


```{r message=FALSE}
require(fma)


Dec = rep(c(0, 0, 0, 0,0,0, 0, 0,0,0, 0,1), length.out=length(beer) )
Feb = rep(c(0,1, 0, 0,0,0, 0, 0,0,0, 0,0), length.out=length(beer) )
Mar = rep(c(0, 0, 1, 0,0,0, 0, 0,0,0, 0,0), length.out=length(beer) )
Apr = rep(c(0, 0, 0, 1,0,0, 0, 0,0,0, 0,0), length.out=length(beer) )
May = rep(c(0, 0, 0, 0,1,0, 0, 0,0,0, 0,0), length.out=length(beer) )
Jun = rep(c(0, 0, 0, 0,0,1, 0, 0,0,0, 0,0), length.out=length(beer) )
Jul = rep(c(0, 0, 0, 0,0,0, 1, 0,0,0, 0,0), length.out=length(beer) )
Aug = rep(c(0, 0, 0, 0,0,0, 0, 1,0,0, 0,0), length.out=length(beer) )
Sep= rep(c(0, 0, 0, 0,0,0, 0, 0,1,0, 0,0), length.out=length(beer) )
Oct= rep(c(0, 0, 0, 0,0,0, 0, 0,0,1, 0,0), length.out=length(beer) )
Nov= rep(c(0, 0, 0, 0,0,0, 0, 0,0,0, 1,0), length.out=length(beer) )


lm.beer<-lm(beer~Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+Nov+Dec)
summary(lm.beer)

tslm.beer<-tslm(beer~ season)

summary(tslm.beer)
plot(forecast(tslm.beer, h=20))

```

# Example with *airpass* time series

The model fitted corresponds to:
$$
\text{airpass}_t=\underbrace{\beta_0 + \beta_1 \ t + \beta_2 \ \text{Feb}_t+ \cdots+\beta_{12} \ \text{Dec}_t }_{\widehat{\text{beer}}_t}+\epsilon_t
$$

```{r message=FALSE}


tslm.airpass<-tslm(airpass~ trend + season)

summary(tslm.airpass)
plot(forecast(tslm.airpass, h=20))

```