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Auto-Arima creates a straight line help
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I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
This is what i've got so far
install.packages("forecast")
install.packages("scales")
library(forecast)
datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
monto=datos$monto.XVI
montots<-ts(monto)
montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
montots
plot(montots)
auto.arima(montots)
fit=arima(montots,order=c(0,1,0))
a=forecast(fit,h=5)
plot(forecast(fit,h=5))
So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
my data looks like this
thank you
r time-series forecasting arima prediction
asked yesterday
sarah lopez
111
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sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
up vote
2
down vote
favorite
I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
This is what i've got so far
install.packages("forecast")
install.packages("scales")
library(forecast)
datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
monto=datos$monto.XVI
montots<-ts(monto)
montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
montots
plot(montots)
auto.arima(montots)
fit=arima(montots,order=c(0,1,0))
a=forecast(fit,h=5)
plot(forecast(fit,h=5))
So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
my data looks like this
thank you
r time-series forecasting arima prediction
asked yesterday
sarah lopez
111
New contributor
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
This is what i've got so far
install.packages("forecast")
install.packages("scales")
library(forecast)
datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
monto=datos$monto.XVI
montots<-ts(monto)
montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
montots
plot(montots)
auto.arima(montots)
fit=arima(montots,order=c(0,1,0))
a=forecast(fit,h=5)
plot(forecast(fit,h=5))
So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
my data looks like this
thank you
r time-series forecasting arima prediction
asked yesterday
sarah lopez
111
New contributor
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :)
This is what i've got so far
install.packages("forecast")
install.packages("scales")
library(forecast)
datos <-read.csv("C:/Users/sarit/Documents/SÉPTIMO CUATRI/iieg/dator.csv",header=T)
monto=datos$monto.XVI
montots<-ts(monto)
montots<-ts(monto,frequency = 12,start = c(2007,1), end = c(2018,8))
montots
plot(montots)
auto.arima(montots)
fit=arima(montots,order=c(0,1,0))
a=forecast(fit,h=5)
plot(forecast(fit,h=5))
So basically, with the autoarima function i get (0,1,0), and when i plot the forecast i get a straight line like this:
my data looks like this
thank you
r time-series forecasting arima prediction
r time-series forecasting arima prediction
asked yesterday
sarah lopez
111
New contributor
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
sarah lopez
111
New contributor
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
sarah lopez
111
New contributor
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
sarah lopez
111
asked yesterday
sarah lopez
111
111
New contributor
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
sarah lopez is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
up vote
6
down vote
Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.
By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:
$$ y_t-y_{t-1} = epsilon_t, $$
or
$$ y_t=y_{t-1} + epsilon_t. $$
That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.
For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:
$$ hat{y}_t=y_{t-1}. $$
And this is iterated. You end up with a flat line.
Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?
You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
answered 23 hours ago
Stephan Kolassa
43k690157
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
6
down vote
Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.
By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:
$$ y_t-y_{t-1} = epsilon_t, $$
or
$$ y_t=y_{t-1} + epsilon_t. $$
That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.
For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:
$$ hat{y}_t=y_{t-1}. $$
And this is iterated. You end up with a flat line.
Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?
You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
answered 23 hours ago
Stephan Kolassa
43k690157
add a comment |
up vote
6
down vote
Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.
By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:
$$ y_t-y_{t-1} = epsilon_t, $$
or
$$ y_t=y_{t-1} + epsilon_t. $$
That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.
For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:
$$ hat{y}_t=y_{t-1}. $$
And this is iterated. You end up with a flat line.
Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?
You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
answered 23 hours ago
Stephan Kolassa
43k690157
add a comment |
up vote
6
down vote
up vote
6
down vote
Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.
By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:
$$ y_t-y_{t-1} = epsilon_t, $$
or
$$ y_t=y_{t-1} + epsilon_t. $$
That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.
For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:
$$ hat{y}_t=y_{t-1}. $$
And this is iterated. You end up with a flat line.
Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?
You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
answered 23 hours ago
Stephan Kolassa
43k690157
Note first of all that your plot does not come from a call to auto.arima(), but from one to arima(). There is a difference.
By supplying order=c(0,1,0) to arima(), you tell it to fit a model of the following type:
$$ y_t-y_{t-1} = epsilon_t, $$
or
$$ y_t=y_{t-1} + epsilon_t. $$
That is, you believe that the increments over the last observation follow a normal distribution, $epsilon_tsim N(0,sigma^2)$.
For your point forecast, forecast() will use the expected value for $epsilon_t$. Which is zero. So your next forecast is simply the last observation:
$$ hat{y}_t=y_{t-1}. $$
And this is iterated. You end up with a flat line.
Try actually fitting using auto.arima(). However, your time series does not exhibit any obvious structure, like trend or seasonality. (Autoregressive or moving average behavior are harder to spot by eye.) In such a situation, a flat line may well be the best forecast: Is it unusual for the MEAN to outperform ARIMA?
You may be interested in the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
answered 23 hours ago
Stephan Kolassa
43k690157
answered 23 hours ago
Stephan Kolassa
43k690157
answered 23 hours ago
Stephan Kolassa
43k690157
answered 23 hours ago
Stephan Kolassa
43k690157
43k690157
add a comment |
add a comment |
sarah lopez is a new contributor. Be nice, and check out our Code of Conduct.
sarah lopez is a new contributor. Be nice, and check out our Code of Conduct.
sarah lopez is a new contributor. Be nice, and check out our Code of Conduct.
sarah lopez is a new contributor. Be nice, and check out our Code of Conduct.
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