Why is arima in R one time step off?One step ahead forecast with new data collected sequentiallyauto.arima and predictionWhy are fitted values different from one-step ahead forecasts?Why can't my (auto.)arima-model forecast my time series?ARIMA: extract date/time information from ARIMA model(S)ARIMA — Hints with Time SeriesOne-Step Ahead ForecastARIMA(1,0,0) one-step ahead prediction in R/forecastARIMA forecasts are way offARIMA predicts the one step ahead of the actual prediction
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Why is arima in R one time step off?
One step ahead forecast with new data collected sequentiallyauto.arima and predictionWhy are fitted values different from one-step ahead forecasts?Why can't my (auto.)arima-model forecast my time series?ARIMA: extract date/time information from ARIMA model(S)ARIMA — Hints with Time SeriesOne-Step Ahead ForecastARIMA(1,0,0) one-step ahead prediction in R/forecastARIMA forecasts are way offARIMA predicts the one step ahead of the actual prediction
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
$begingroup$
I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data
library(forecast)
library(ggplot2)
mod_arima <- arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
Now, if we use forecast
to get the fit on the model, it's a year off. Compare these two plots:
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = fitted(mod_arima),
alpha = 0.5, lwd = 2, color = "blue")
To one where we delete the first value and tack on an NA at the end
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = c(fitted(mod_arima)[-1], NA),
alpha = 0.5, lwd = 2, color = "blue")
The second lines up perfectly, while the first is obviously one year off. What's going on here?
r time-series forecasting arima
$endgroup$
add a comment |
$begingroup$
I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data
library(forecast)
library(ggplot2)
mod_arima <- arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
Now, if we use forecast
to get the fit on the model, it's a year off. Compare these two plots:
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = fitted(mod_arima),
alpha = 0.5, lwd = 2, color = "blue")
To one where we delete the first value and tack on an NA at the end
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = c(fitted(mod_arima)[-1], NA),
alpha = 0.5, lwd = 2, color = "blue")
The second lines up perfectly, while the first is obviously one year off. What's going on here?
r time-series forecasting arima
$endgroup$
3
$begingroup$
This is completely normal if the best prediction of $y_t+1$ is roughly $y_t$, which happens when the time series is a martingale difference sequence (typical e.g. for prices of shares and other financial instruments).
$endgroup$
– Richard Hardy
Apr 23 at 17:01
add a comment |
$begingroup$
I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data
library(forecast)
library(ggplot2)
mod_arima <- arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
Now, if we use forecast
to get the fit on the model, it's a year off. Compare these two plots:
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = fitted(mod_arima),
alpha = 0.5, lwd = 2, color = "blue")
To one where we delete the first value and tack on an NA at the end
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = c(fitted(mod_arima)[-1], NA),
alpha = 0.5, lwd = 2, color = "blue")
The second lines up perfectly, while the first is obviously one year off. What's going on here?
r time-series forecasting arima
$endgroup$
I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data
library(forecast)
library(ggplot2)
mod_arima <- arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
Now, if we use forecast
to get the fit on the model, it's a year off. Compare these two plots:
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = fitted(mod_arima),
alpha = 0.5, lwd = 2, color = "blue")
To one where we delete the first value and tack on an NA at the end
ggplot(sun_dat,
aes(x = years, y = sunspots)) +
geom_line() +
geom_line(y = c(fitted(mod_arima)[-1], NA),
alpha = 0.5, lwd = 2, color = "blue")
The second lines up perfectly, while the first is obviously one year off. What's going on here?
r time-series forecasting arima
r time-series forecasting arima
asked Apr 23 at 16:49
jebyrnesjebyrnes
593415
593415
3
$begingroup$
This is completely normal if the best prediction of $y_t+1$ is roughly $y_t$, which happens when the time series is a martingale difference sequence (typical e.g. for prices of shares and other financial instruments).
$endgroup$
– Richard Hardy
Apr 23 at 17:01
add a comment |
3
$begingroup$
This is completely normal if the best prediction of $y_t+1$ is roughly $y_t$, which happens when the time series is a martingale difference sequence (typical e.g. for prices of shares and other financial instruments).
$endgroup$
– Richard Hardy
Apr 23 at 17:01
3
3
$begingroup$
This is completely normal if the best prediction of $y_t+1$ is roughly $y_t$, which happens when the time series is a martingale difference sequence (typical e.g. for prices of shares and other financial instruments).
$endgroup$
– Richard Hardy
Apr 23 at 17:01
$begingroup$
This is completely normal if the best prediction of $y_t+1$ is roughly $y_t$, which happens when the time series is a martingale difference sequence (typical e.g. for prices of shares and other financial instruments).
$endgroup$
– Richard Hardy
Apr 23 at 17:01
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
As Richard Hardy writes: if your prediction $haty_t+1$ of $y_t+1$ is pretty much your last observation $y_t$, then of course you would expect $haty_t+1$ to line up with $y_t$, which would show exactly as the one year lag you wonder about.
And if you specify
arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
then you fitted exactly that: an AR(1) model. The AR(1) coefficient is estimated to be about 0.81. (With an intercept. Also, if you add the year as a regressor, you will model a trend. Did you intend to do this?)
Incidentally, if you allow auto.arima()
to fit a model, it will choose an ARIMA(2,1,3) model, which will not exhibit this lag:
library(forecast)
model <- auto.arima(sunspot.year)
plot(sunspot.year)
lines(model$fit,col="red")
You could also include the known sunspot period of length 11, though auto.arima()
won't automatically fit a SARIMA.
$endgroup$
add a comment |
$begingroup$
You're fitting an $ARIMA(1,0,0)$ model to your data, which means that your fitted model has the form:
$hatY_t+1-m = a(Y_t-m) + epsilon$
So it looks like it's a year off, because all the model is doing is copying the value from the current year $Y_t$, with an adjustment $a$ and a drift term, and making that the prediction for the next year $hatY_t+1$.
Your data looks highly cyclical, you might want to try fitting a seasonal ARIMA model instead of a simple AR(1) or AR(2).
$endgroup$
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
As Richard Hardy writes: if your prediction $haty_t+1$ of $y_t+1$ is pretty much your last observation $y_t$, then of course you would expect $haty_t+1$ to line up with $y_t$, which would show exactly as the one year lag you wonder about.
And if you specify
arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
then you fitted exactly that: an AR(1) model. The AR(1) coefficient is estimated to be about 0.81. (With an intercept. Also, if you add the year as a regressor, you will model a trend. Did you intend to do this?)
Incidentally, if you allow auto.arima()
to fit a model, it will choose an ARIMA(2,1,3) model, which will not exhibit this lag:
library(forecast)
model <- auto.arima(sunspot.year)
plot(sunspot.year)
lines(model$fit,col="red")
You could also include the known sunspot period of length 11, though auto.arima()
won't automatically fit a SARIMA.
$endgroup$
add a comment |
$begingroup$
As Richard Hardy writes: if your prediction $haty_t+1$ of $y_t+1$ is pretty much your last observation $y_t$, then of course you would expect $haty_t+1$ to line up with $y_t$, which would show exactly as the one year lag you wonder about.
And if you specify
arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
then you fitted exactly that: an AR(1) model. The AR(1) coefficient is estimated to be about 0.81. (With an intercept. Also, if you add the year as a regressor, you will model a trend. Did you intend to do this?)
Incidentally, if you allow auto.arima()
to fit a model, it will choose an ARIMA(2,1,3) model, which will not exhibit this lag:
library(forecast)
model <- auto.arima(sunspot.year)
plot(sunspot.year)
lines(model$fit,col="red")
You could also include the known sunspot period of length 11, though auto.arima()
won't automatically fit a SARIMA.
$endgroup$
add a comment |
$begingroup$
As Richard Hardy writes: if your prediction $haty_t+1$ of $y_t+1$ is pretty much your last observation $y_t$, then of course you would expect $haty_t+1$ to line up with $y_t$, which would show exactly as the one year lag you wonder about.
And if you specify
arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
then you fitted exactly that: an AR(1) model. The AR(1) coefficient is estimated to be about 0.81. (With an intercept. Also, if you add the year as a regressor, you will model a trend. Did you intend to do this?)
Incidentally, if you allow auto.arima()
to fit a model, it will choose an ARIMA(2,1,3) model, which will not exhibit this lag:
library(forecast)
model <- auto.arima(sunspot.year)
plot(sunspot.year)
lines(model$fit,col="red")
You could also include the known sunspot period of length 11, though auto.arima()
won't automatically fit a SARIMA.
$endgroup$
As Richard Hardy writes: if your prediction $haty_t+1$ of $y_t+1$ is pretty much your last observation $y_t$, then of course you would expect $haty_t+1$ to line up with $y_t$, which would show exactly as the one year lag you wonder about.
And if you specify
arima(sunspot.year, c(1, 0, 0), xreg = 1700:1988)
then you fitted exactly that: an AR(1) model. The AR(1) coefficient is estimated to be about 0.81. (With an intercept. Also, if you add the year as a regressor, you will model a trend. Did you intend to do this?)
Incidentally, if you allow auto.arima()
to fit a model, it will choose an ARIMA(2,1,3) model, which will not exhibit this lag:
library(forecast)
model <- auto.arima(sunspot.year)
plot(sunspot.year)
lines(model$fit,col="red")
You could also include the known sunspot period of length 11, though auto.arima()
won't automatically fit a SARIMA.
answered Apr 23 at 17:24
Stephan KolassaStephan Kolassa
48.7k8102185
48.7k8102185
add a comment |
add a comment |
$begingroup$
You're fitting an $ARIMA(1,0,0)$ model to your data, which means that your fitted model has the form:
$hatY_t+1-m = a(Y_t-m) + epsilon$
So it looks like it's a year off, because all the model is doing is copying the value from the current year $Y_t$, with an adjustment $a$ and a drift term, and making that the prediction for the next year $hatY_t+1$.
Your data looks highly cyclical, you might want to try fitting a seasonal ARIMA model instead of a simple AR(1) or AR(2).
$endgroup$
add a comment |
$begingroup$
You're fitting an $ARIMA(1,0,0)$ model to your data, which means that your fitted model has the form:
$hatY_t+1-m = a(Y_t-m) + epsilon$
So it looks like it's a year off, because all the model is doing is copying the value from the current year $Y_t$, with an adjustment $a$ and a drift term, and making that the prediction for the next year $hatY_t+1$.
Your data looks highly cyclical, you might want to try fitting a seasonal ARIMA model instead of a simple AR(1) or AR(2).
$endgroup$
add a comment |
$begingroup$
You're fitting an $ARIMA(1,0,0)$ model to your data, which means that your fitted model has the form:
$hatY_t+1-m = a(Y_t-m) + epsilon$
So it looks like it's a year off, because all the model is doing is copying the value from the current year $Y_t$, with an adjustment $a$ and a drift term, and making that the prediction for the next year $hatY_t+1$.
Your data looks highly cyclical, you might want to try fitting a seasonal ARIMA model instead of a simple AR(1) or AR(2).
$endgroup$
You're fitting an $ARIMA(1,0,0)$ model to your data, which means that your fitted model has the form:
$hatY_t+1-m = a(Y_t-m) + epsilon$
So it looks like it's a year off, because all the model is doing is copying the value from the current year $Y_t$, with an adjustment $a$ and a drift term, and making that the prediction for the next year $hatY_t+1$.
Your data looks highly cyclical, you might want to try fitting a seasonal ARIMA model instead of a simple AR(1) or AR(2).
edited Apr 23 at 18:46
Stephan Kolassa
48.7k8102185
48.7k8102185
answered Apr 23 at 17:24
Skander H.Skander H.
4,0251233
4,0251233
add a comment |
add a comment |
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$begingroup$
This is completely normal if the best prediction of $y_t+1$ is roughly $y_t$, which happens when the time series is a martingale difference sequence (typical e.g. for prices of shares and other financial instruments).
$endgroup$
– Richard Hardy
Apr 23 at 17:01