Time series decomposition
$begingroup$
For me, the original data looks to have like a decreasing or constant trend but stl() is giving a different trend altogether. Can someone here please explain why?
The decomposition plot is as follows:
time-series
edited Jan 9 at 16:05
Mark.F
766218
asked Jan 8 at 11:08
KunalKunal
61
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bumped to the homepage by Community♦ 8 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
For me, the original data looks to have like a decreasing or constant trend but stl() is giving a different trend altogether. Can someone here please explain why?
The decomposition plot is as follows:
time-series
edited Jan 9 at 16:05
Mark.F
766218
asked Jan 8 at 11:08
KunalKunal
61
$endgroup$
bumped to the homepage by Community♦ 8 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
For me, the original data looks to have like a decreasing or constant trend but stl() is giving a different trend altogether. Can someone here please explain why?
The decomposition plot is as follows:
time-series
edited Jan 9 at 16:05
Mark.F
766218
asked Jan 8 at 11:08
KunalKunal
61
$endgroup$
For me, the original data looks to have like a decreasing or constant trend but stl() is giving a different trend altogether. Can someone here please explain why?
The decomposition plot is as follows:
time-series
time-series
edited Jan 9 at 16:05
Mark.F
766218
asked Jan 8 at 11:08
KunalKunal
61
edited Jan 9 at 16:05
Mark.F
766218
asked Jan 8 at 11:08
KunalKunal
61
edited Jan 9 at 16:05
Mark.F
766218
edited Jan 9 at 16:05
Mark.F
766218
edited Jan 9 at 16:05
Mark.F
766218
766218
asked Jan 8 at 11:08
KunalKunal
61
asked Jan 8 at 11:08
KunalKunal
61
asked Jan 8 at 11:08
KunalKunal
61
61
bumped to the homepage by Community♦ 8 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 8 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
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$begingroup$
To my eye, the second half of 2018 is substantially higher than the prior two years, so the trend that stl() is giving seems not unreasonable.
Trying to fit a time series model to only three years of data is going to require you to take a pretty strong hand guiding the model; I wouldn't expect a turn-key solution to give satisfactory results.
One obvious problem with the decomposition from stl() is that the seasonal figure is wildly overfit. I would try using the Fourier series approach to fitting the seasonal figure that Rob Hyndman describes in this blog post. I have applied that technique in exactly your situation and gotten decent results.
The closest to a turn-key solution is probably going to Facebook's prophet library. But with only three years of data, I still suspect you'll overfit the seasonal component if you call it with the defaults.
If you post your actual data and describe your goals more completely, folks might be able to help more.
answered Jan 8 at 16:19
John RauserJohn Rauser
914
$endgroup$
add a comment |
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1 Answer
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1 Answer
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$begingroup$
To my eye, the second half of 2018 is substantially higher than the prior two years, so the trend that stl() is giving seems not unreasonable.
Trying to fit a time series model to only three years of data is going to require you to take a pretty strong hand guiding the model; I wouldn't expect a turn-key solution to give satisfactory results.
One obvious problem with the decomposition from stl() is that the seasonal figure is wildly overfit. I would try using the Fourier series approach to fitting the seasonal figure that Rob Hyndman describes in this blog post. I have applied that technique in exactly your situation and gotten decent results.
The closest to a turn-key solution is probably going to Facebook's prophet library. But with only three years of data, I still suspect you'll overfit the seasonal component if you call it with the defaults.
If you post your actual data and describe your goals more completely, folks might be able to help more.
answered Jan 8 at 16:19
John RauserJohn Rauser
914
$endgroup$
add a comment |
$begingroup$
To my eye, the second half of 2018 is substantially higher than the prior two years, so the trend that stl() is giving seems not unreasonable.
Trying to fit a time series model to only three years of data is going to require you to take a pretty strong hand guiding the model; I wouldn't expect a turn-key solution to give satisfactory results.
One obvious problem with the decomposition from stl() is that the seasonal figure is wildly overfit. I would try using the Fourier series approach to fitting the seasonal figure that Rob Hyndman describes in this blog post. I have applied that technique in exactly your situation and gotten decent results.
The closest to a turn-key solution is probably going to Facebook's prophet library. But with only three years of data, I still suspect you'll overfit the seasonal component if you call it with the defaults.
If you post your actual data and describe your goals more completely, folks might be able to help more.
answered Jan 8 at 16:19
John RauserJohn Rauser
914
$endgroup$
add a comment |
$begingroup$
To my eye, the second half of 2018 is substantially higher than the prior two years, so the trend that stl() is giving seems not unreasonable.
Trying to fit a time series model to only three years of data is going to require you to take a pretty strong hand guiding the model; I wouldn't expect a turn-key solution to give satisfactory results.
One obvious problem with the decomposition from stl() is that the seasonal figure is wildly overfit. I would try using the Fourier series approach to fitting the seasonal figure that Rob Hyndman describes in this blog post. I have applied that technique in exactly your situation and gotten decent results.
The closest to a turn-key solution is probably going to Facebook's prophet library. But with only three years of data, I still suspect you'll overfit the seasonal component if you call it with the defaults.
If you post your actual data and describe your goals more completely, folks might be able to help more.
answered Jan 8 at 16:19
John RauserJohn Rauser
914
$endgroup$
To my eye, the second half of 2018 is substantially higher than the prior two years, so the trend that stl() is giving seems not unreasonable.
Trying to fit a time series model to only three years of data is going to require you to take a pretty strong hand guiding the model; I wouldn't expect a turn-key solution to give satisfactory results.
One obvious problem with the decomposition from stl() is that the seasonal figure is wildly overfit. I would try using the Fourier series approach to fitting the seasonal figure that Rob Hyndman describes in this blog post. I have applied that technique in exactly your situation and gotten decent results.
The closest to a turn-key solution is probably going to Facebook's prophet library. But with only three years of data, I still suspect you'll overfit the seasonal component if you call it with the defaults.
If you post your actual data and describe your goals more completely, folks might be able to help more.
answered Jan 8 at 16:19
John RauserJohn Rauser
914
answered Jan 8 at 16:19
John RauserJohn Rauser
914
answered Jan 8 at 16:19
John RauserJohn Rauser
914
answered Jan 8 at 16:19
John RauserJohn Rauser
914
914
add a comment |
add a comment |
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