Is it good practice to use Linear Least-Squares with SMA?How to correctly apply a linear trendline...
What exactly is this small puffer fish doing and how did it manage to accomplish such a feat?
How do I change two letters closest to a string and one letter immediately after a string using Notepad++?
What is "focus distance lower/upper" and how is it different from depth of field?
Is a party consisting of only a bard, a cleric, and a warlock functional long-term?
I got the following comment from a reputed math journal. What does it mean?
World War I as a war of liberals against authoritarians?
Violin - Can double stops be played when the strings are not next to each other?
Are ETF trackers fundamentally better than individual stocks?
Is "upgrade" the right word to use in this context?
Simplify an interface for flexibly applying rules to periods of time
Professor being mistaken for a grad student
Employee lack of ownership
This word with a lot of past tenses
Why do passenger jet manufacturers design their planes with stall prevention systems?
What is the significance behind "40 days" that often appears in the Bible?
Instead of a Universal Basic Income program, why not implement a "Universal Basic Needs" program?
Why Choose Less Effective Armour Types?
Why does overlay work only on the first tcolorbox?
Are all passive ability checks floors for active ability checks?
Python if-else code style for reduced code for rounding floats
Is there a hypothetical scenario that would make Earth uninhabitable for humans, but not for (the majority of) other animals?
Happy pi day, everyone!
combinatorics floor summation
Recruiter wants very extensive technical details about all of my previous work
Is it good practice to use Linear Least-Squares with SMA?
How to correctly apply a linear trendline equationMeasuring treatment effect on top-ranked subjects selected at point in time from longitudinal dataEnsemble model performs better with worse performing consitutent models?Textbooks on linear regression with least squaresInterpreting regression and $R^2$ with small $n$Solution to force a polynomial curve to end at a specific locationLinear Regression Understanding Least SquaresCan residuals be calculated from N-point moving averages or just the regression line? Also, what is the standard way to determine regression line?Line of best fit does not look like a good fit. Why?Linear least squares algorithms
$begingroup$
I have time-series (daily) data and I want to understand the general trend.
My current approach is:
Calculate the 7-day simple moving average.
Add a line of best fit (linear least squares regression).
Plot, then review metrics such as r, r^2, etc.
Question: is it good practice to draw a line-of-best fit on a moving average? I'm not very experienced but my understanding is MA and linear trend lines are both trend lines, so I'm not sure if it's OK to combine them in this way.
Raw data looks like this:
day + count
2015-01-01 | 123
2015-01-02 | 290
2015-01-03 | 329
2015-01-04 | 276
Let me know if more detail would help- any direction on this is much appreciated.
regression time-series correlation trend moving-average
asked 2 hours ago
Chef36Chef36
61
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have time-series (daily) data and I want to understand the general trend.
My current approach is:
Calculate the 7-day simple moving average.
Add a line of best fit (linear least squares regression).
Plot, then review metrics such as r, r^2, etc.
Question: is it good practice to draw a line-of-best fit on a moving average? I'm not very experienced but my understanding is MA and linear trend lines are both trend lines, so I'm not sure if it's OK to combine them in this way.
Raw data looks like this:
day + count
2015-01-01 | 123
2015-01-02 | 290
2015-01-03 | 329
2015-01-04 | 276
Let me know if more detail would help- any direction on this is much appreciated.
regression time-series correlation trend moving-average
asked 2 hours ago
Chef36Chef36
61
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
It depends on how you compute step (2), "a line of best fit (linear least squares regression)." If you just drop the data into a least squares black box, most likely it operates under the assumption the errors are independent, whereas in a moving average the errors are strongly interdependent (e.g., neighboring 7-day averages have six days of data in common). You need to use a procedure that accounts for this. There are robust ways to explore trends, such as various nonparametric smoothers, so maybe it would be more fruitful to investigate them rather than fixing your current approach.
$endgroup$
– whuber♦
2 hours ago
add a comment |
$begingroup$
I have time-series (daily) data and I want to understand the general trend.
My current approach is:
Calculate the 7-day simple moving average.
Add a line of best fit (linear least squares regression).
Plot, then review metrics such as r, r^2, etc.
Question: is it good practice to draw a line-of-best fit on a moving average? I'm not very experienced but my understanding is MA and linear trend lines are both trend lines, so I'm not sure if it's OK to combine them in this way.
Raw data looks like this:
day + count
2015-01-01 | 123
2015-01-02 | 290
2015-01-03 | 329
2015-01-04 | 276
Let me know if more detail would help- any direction on this is much appreciated.
regression time-series correlation trend moving-average
asked 2 hours ago
Chef36Chef36
61
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have time-series (daily) data and I want to understand the general trend.
My current approach is:
Calculate the 7-day simple moving average.
Add a line of best fit (linear least squares regression).
Plot, then review metrics such as r, r^2, etc.
Question: is it good practice to draw a line-of-best fit on a moving average? I'm not very experienced but my understanding is MA and linear trend lines are both trend lines, so I'm not sure if it's OK to combine them in this way.
Raw data looks like this:
day + count
2015-01-01 | 123
2015-01-02 | 290
2015-01-03 | 329
2015-01-04 | 276
Let me know if more detail would help- any direction on this is much appreciated.
regression time-series correlation trend moving-average
regression time-series correlation trend moving-average
asked 2 hours ago
Chef36Chef36
61
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
Chef36Chef36
61
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
Chef36Chef36
61
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
Chef36Chef36
61
asked 2 hours ago
Chef36Chef36
61
61
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Chef36 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
1
$begingroup$
It depends on how you compute step (2), "a line of best fit (linear least squares regression)." If you just drop the data into a least squares black box, most likely it operates under the assumption the errors are independent, whereas in a moving average the errors are strongly interdependent (e.g., neighboring 7-day averages have six days of data in common). You need to use a procedure that accounts for this. There are robust ways to explore trends, such as various nonparametric smoothers, so maybe it would be more fruitful to investigate them rather than fixing your current approach.
$endgroup$
– whuber♦
2 hours ago
add a comment |
1
$begingroup$
It depends on how you compute step (2), "a line of best fit (linear least squares regression)." If you just drop the data into a least squares black box, most likely it operates under the assumption the errors are independent, whereas in a moving average the errors are strongly interdependent (e.g., neighboring 7-day averages have six days of data in common). You need to use a procedure that accounts for this. There are robust ways to explore trends, such as various nonparametric smoothers, so maybe it would be more fruitful to investigate them rather than fixing your current approach.
$endgroup$
– whuber♦
2 hours ago
1
1
$begingroup$
It depends on how you compute step (2), "a line of best fit (linear least squares regression)." If you just drop the data into a least squares black box, most likely it operates under the assumption the errors are independent, whereas in a moving average the errors are strongly interdependent (e.g., neighboring 7-day averages have six days of data in common). You need to use a procedure that accounts for this. There are robust ways to explore trends, such as various nonparametric smoothers, so maybe it would be more fruitful to investigate them rather than fixing your current approach.
$endgroup$
– whuber♦
2 hours ago
$begingroup$
It depends on how you compute step (2), "a line of best fit (linear least squares regression)." If you just drop the data into a least squares black box, most likely it operates under the assumption the errors are independent, whereas in a moving average the errors are strongly interdependent (e.g., neighboring 7-day averages have six days of data in common). You need to use a procedure that accounts for this. There are robust ways to explore trends, such as various nonparametric smoothers, so maybe it would be more fruitful to investigate them rather than fixing your current approach.
$endgroup$
– whuber♦
2 hours ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Of course you can do a fit on a moving average. That is your right. But the statistical diagnostics are not reliable anymore. The reason is that the IID property required in standard OLS are violated when you apply plain regression on a quantity that is highly autocorrelated.
Your $r^2$ will be artificially high and will insinuate a false sense of statistical significance. Think about this case, instead of a moving average do a linear fit on your original data in time first. And then do it a gain, you will get 100% $r^2$.
These models are not reliable ex-ante predictors and will have very low out-of-sample qualities.
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
$endgroup$
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "65"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Chef36 is a new contributor. Be nice, and check out our Code of Conduct.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f397917%2fis-it-good-practice-to-use-linear-least-squares-with-sma%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Of course you can do a fit on a moving average. That is your right. But the statistical diagnostics are not reliable anymore. The reason is that the IID property required in standard OLS are violated when you apply plain regression on a quantity that is highly autocorrelated.
Your $r^2$ will be artificially high and will insinuate a false sense of statistical significance. Think about this case, instead of a moving average do a linear fit on your original data in time first. And then do it a gain, you will get 100% $r^2$.
These models are not reliable ex-ante predictors and will have very low out-of-sample qualities.
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
$endgroup$
add a comment |
$begingroup$
Of course you can do a fit on a moving average. That is your right. But the statistical diagnostics are not reliable anymore. The reason is that the IID property required in standard OLS are violated when you apply plain regression on a quantity that is highly autocorrelated.
Your $r^2$ will be artificially high and will insinuate a false sense of statistical significance. Think about this case, instead of a moving average do a linear fit on your original data in time first. And then do it a gain, you will get 100% $r^2$.
These models are not reliable ex-ante predictors and will have very low out-of-sample qualities.
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
$endgroup$
add a comment |
$begingroup$
Of course you can do a fit on a moving average. That is your right. But the statistical diagnostics are not reliable anymore. The reason is that the IID property required in standard OLS are violated when you apply plain regression on a quantity that is highly autocorrelated.
Your $r^2$ will be artificially high and will insinuate a false sense of statistical significance. Think about this case, instead of a moving average do a linear fit on your original data in time first. And then do it a gain, you will get 100% $r^2$.
These models are not reliable ex-ante predictors and will have very low out-of-sample qualities.
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
$endgroup$
Of course you can do a fit on a moving average. That is your right. But the statistical diagnostics are not reliable anymore. The reason is that the IID property required in standard OLS are violated when you apply plain regression on a quantity that is highly autocorrelated.
Your $r^2$ will be artificially high and will insinuate a false sense of statistical significance. Think about this case, instead of a moving average do a linear fit on your original data in time first. And then do it a gain, you will get 100% $r^2$.
These models are not reliable ex-ante predictors and will have very low out-of-sample qualities.
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
answered 2 hours ago
Gkhan CebsGkhan Cebs
1443
1443
add a comment |
add a comment |
Chef36 is a new contributor. Be nice, and check out our Code of Conduct.
Chef36 is a new contributor. Be nice, and check out our Code of Conduct.
Chef36 is a new contributor. Be nice, and check out our Code of Conduct.
Chef36 is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Cross Validated!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f397917%2fis-it-good-practice-to-use-linear-least-squares-with-sma%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
1
$begingroup$
It depends on how you compute step (2), "a line of best fit (linear least squares regression)." If you just drop the data into a least squares black box, most likely it operates under the assumption the errors are independent, whereas in a moving average the errors are strongly interdependent (e.g., neighboring 7-day averages have six days of data in common). You need to use a procedure that accounts for this. There are robust ways to explore trends, such as various nonparametric smoothers, so maybe it would be more fruitful to investigate them rather than fixing your current approach.
$endgroup$
– whuber♦
2 hours ago