Determining if time series follows a pattern Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHow to deal with time series which change in seasonality or other patterns?Forecasting an individual based on a representative groupTime series data: How I measure influence of new product sales on existing product sales (statistically)?Average and standard deviations around timestampsHow to model to predict hotel booking abnomality?Detecting anomalies from time series correlated variableswhat is the best approach to my prediction problemClassification/Prediction based on Multivariate Time SeriesTime series forecasting using multiple time series as training dataWhat Machine Learning Algorithm could I use to determine some measure in a date?
What *exactly* is electrical current, voltage, and resistance?
Why doesn't the university give past final exams' answers?
If gravity precedes the formation of a solar system, where did the mass come from that caused the gravity?
Does using the Inspiration rules for character defects encourage My Guy Syndrome?
Why aren't road bike wheels tiny?
“Since the train was delayed for more than an hour, passengers were given a full refund.” – Why is there no article before “passengers”?
2 sample t test for sample sizes - 30,000 and 150,000
What could prevent concentrated local exploration?
Why did Bronn offer to be Tyrion Lannister's champion in trial by combat?
Can I take recommendation from someone I met at a conference?
Coin Game with infinite paradox
How to ask rejected full-time candidates to apply to teach individual courses?
How to leave only the following strings?
"Destructive force" carried by a B-52?
Like totally amazing interchangeable sister outfit accessory swapping or whatever
When does Bran Stark remember Jamie pushing him?
Lights are flickering on and off after accidentally bumping into light switch
Protagonist's race is hidden - should I reveal it?
/bin/ls sorts differently than just ls
What kind of equipment or other technology is necessary to photograph sprites (atmospheric phenomenon)
Are Flameskulls resistant to magical piercing damage?
Why these surprising proportionalities of integrals involving odd zeta values?
Why is one lightbulb in a string illuminated?
Does traveling In The United States require a passport or can I use my green card if not a US citizen?
Determining if time series follows a pattern
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow to deal with time series which change in seasonality or other patterns?Forecasting an individual based on a representative groupTime series data: How I measure influence of new product sales on existing product sales (statistically)?Average and standard deviations around timestampsHow to model to predict hotel booking abnomality?Detecting anomalies from time series correlated variableswhat is the best approach to my prediction problemClassification/Prediction based on Multivariate Time SeriesTime series forecasting using multiple time series as training dataWhat Machine Learning Algorithm could I use to determine some measure in a date?
$begingroup$
I was wondering if anyone had any idea how to solve this problem.
So basically I have a dataset where some person approximately comes at some regular interval and I don't know what that interval is. I need to determine if the person comes in at approximately regular intervals, not necessarily what is the specifically value of the interval. For example if a certain person comes to my house to deliver milk over some period on say
Week 1: Mon, Wed, Fri,
Week 2:Mon, Wed, Fri
Week 3:Mon, Thu, Fri
Week 4:Mon, Wed, Fri
Week 5:Mon, Wed, Fri
Week 6:Mon, Wed, Fri
Week 7:Mon, Wed, Fri
Week 8:Mon, Thu, Fri
So as we can see out of these 8 weeks only in 2 weeks the person didn't come on Wednesday and instead came on Thursday which can be attributed to maybe a holiday the day before. So the solution to this example is that the person does follow a regular pattern.
Similarly this is another example. Say the person came on -
Mon, Thu, Sun, Wed, Sat, Tue, Fri, Mon, Wed, Sun
where this person follows a regular pattern because except for the last Wednesday he comes every fourth day.
This is an example of where a person doesn't follow a pattern, say the person came on
Mon, Wed, Sat, Fri, Thu, Fri, Wed, Fri, Sun, Sun, Sat
I have to do this for hundreds of different people.
Another equivalent problem is if I know which days over a certain period of time (say a month) some person arrives I need to determine if they follow some pattern or not
I thought about trying to fit the data into a sinusoidal curve but I'm not sure if it will work when I have a person who comes every say Mon, Tue, Fri or the 7th of every month or the first and third monday of every month etc.
I'm open to any method as long as it has good accuracy. Also, depending on whichever algorithm you think is best, if possible could you share a link to some code which solves a similar problem so I can get a general idea of how I'm supposed to implement my algorithm. I'm pretty new to machine learning / data science. Thank You!
machine-learning time-series
asked Apr 5 at 5:44
maxachmedmaxachmed
113
$endgroup$
add a comment |
$begingroup$
I was wondering if anyone had any idea how to solve this problem.
So basically I have a dataset where some person approximately comes at some regular interval and I don't know what that interval is. I need to determine if the person comes in at approximately regular intervals, not necessarily what is the specifically value of the interval. For example if a certain person comes to my house to deliver milk over some period on say
Week 1: Mon, Wed, Fri,
Week 2:Mon, Wed, Fri
Week 3:Mon, Thu, Fri
Week 4:Mon, Wed, Fri
Week 5:Mon, Wed, Fri
Week 6:Mon, Wed, Fri
Week 7:Mon, Wed, Fri
Week 8:Mon, Thu, Fri
So as we can see out of these 8 weeks only in 2 weeks the person didn't come on Wednesday and instead came on Thursday which can be attributed to maybe a holiday the day before. So the solution to this example is that the person does follow a regular pattern.
Similarly this is another example. Say the person came on -
Mon, Thu, Sun, Wed, Sat, Tue, Fri, Mon, Wed, Sun
where this person follows a regular pattern because except for the last Wednesday he comes every fourth day.
This is an example of where a person doesn't follow a pattern, say the person came on
Mon, Wed, Sat, Fri, Thu, Fri, Wed, Fri, Sun, Sun, Sat
I have to do this for hundreds of different people.
Another equivalent problem is if I know which days over a certain period of time (say a month) some person arrives I need to determine if they follow some pattern or not
I thought about trying to fit the data into a sinusoidal curve but I'm not sure if it will work when I have a person who comes every say Mon, Tue, Fri or the 7th of every month or the first and third monday of every month etc.
I'm open to any method as long as it has good accuracy. Also, depending on whichever algorithm you think is best, if possible could you share a link to some code which solves a similar problem so I can get a general idea of how I'm supposed to implement my algorithm. I'm pretty new to machine learning / data science. Thank You!
machine-learning time-series
asked Apr 5 at 5:44
maxachmedmaxachmed
113
$endgroup$
add a comment |
$begingroup$
I was wondering if anyone had any idea how to solve this problem.
So basically I have a dataset where some person approximately comes at some regular interval and I don't know what that interval is. I need to determine if the person comes in at approximately regular intervals, not necessarily what is the specifically value of the interval. For example if a certain person comes to my house to deliver milk over some period on say
Week 1: Mon, Wed, Fri,
Week 2:Mon, Wed, Fri
Week 3:Mon, Thu, Fri
Week 4:Mon, Wed, Fri
Week 5:Mon, Wed, Fri
Week 6:Mon, Wed, Fri
Week 7:Mon, Wed, Fri
Week 8:Mon, Thu, Fri
So as we can see out of these 8 weeks only in 2 weeks the person didn't come on Wednesday and instead came on Thursday which can be attributed to maybe a holiday the day before. So the solution to this example is that the person does follow a regular pattern.
Similarly this is another example. Say the person came on -
Mon, Thu, Sun, Wed, Sat, Tue, Fri, Mon, Wed, Sun
where this person follows a regular pattern because except for the last Wednesday he comes every fourth day.
This is an example of where a person doesn't follow a pattern, say the person came on
Mon, Wed, Sat, Fri, Thu, Fri, Wed, Fri, Sun, Sun, Sat
I have to do this for hundreds of different people.
Another equivalent problem is if I know which days over a certain period of time (say a month) some person arrives I need to determine if they follow some pattern or not
I thought about trying to fit the data into a sinusoidal curve but I'm not sure if it will work when I have a person who comes every say Mon, Tue, Fri or the 7th of every month or the first and third monday of every month etc.
I'm open to any method as long as it has good accuracy. Also, depending on whichever algorithm you think is best, if possible could you share a link to some code which solves a similar problem so I can get a general idea of how I'm supposed to implement my algorithm. I'm pretty new to machine learning / data science. Thank You!
machine-learning time-series
asked Apr 5 at 5:44
maxachmedmaxachmed
113
$endgroup$
I was wondering if anyone had any idea how to solve this problem.
So basically I have a dataset where some person approximately comes at some regular interval and I don't know what that interval is. I need to determine if the person comes in at approximately regular intervals, not necessarily what is the specifically value of the interval. For example if a certain person comes to my house to deliver milk over some period on say
Week 1: Mon, Wed, Fri,
Week 2:Mon, Wed, Fri
Week 3:Mon, Thu, Fri
Week 4:Mon, Wed, Fri
Week 5:Mon, Wed, Fri
Week 6:Mon, Wed, Fri
Week 7:Mon, Wed, Fri
Week 8:Mon, Thu, Fri
So as we can see out of these 8 weeks only in 2 weeks the person didn't come on Wednesday and instead came on Thursday which can be attributed to maybe a holiday the day before. So the solution to this example is that the person does follow a regular pattern.
Similarly this is another example. Say the person came on -
Mon, Thu, Sun, Wed, Sat, Tue, Fri, Mon, Wed, Sun
where this person follows a regular pattern because except for the last Wednesday he comes every fourth day.
This is an example of where a person doesn't follow a pattern, say the person came on
Mon, Wed, Sat, Fri, Thu, Fri, Wed, Fri, Sun, Sun, Sat
I have to do this for hundreds of different people.
Another equivalent problem is if I know which days over a certain period of time (say a month) some person arrives I need to determine if they follow some pattern or not
I thought about trying to fit the data into a sinusoidal curve but I'm not sure if it will work when I have a person who comes every say Mon, Tue, Fri or the 7th of every month or the first and third monday of every month etc.
I'm open to any method as long as it has good accuracy. Also, depending on whichever algorithm you think is best, if possible could you share a link to some code which solves a similar problem so I can get a general idea of how I'm supposed to implement my algorithm. I'm pretty new to machine learning / data science. Thank You!
machine-learning time-series
machine-learning time-series
asked Apr 5 at 5:44
maxachmedmaxachmed
113
asked Apr 5 at 5:44
maxachmedmaxachmed
113
edited Apr 5 at 6:08
maxachmed
asked Apr 5 at 5:44
maxachmedmaxachmed
113
asked Apr 5 at 5:44
maxachmedmaxachmed
113
asked Apr 5 at 5:44
maxachmedmaxachmed
113
113
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
It appears as though you are attempting to determine the presence of seasonality in this time series, i.e. a process that repeats itself at regular intervals.
In this regard, it might be a good idea to decompose your time series and check for the presence of seasonality trends.
As an example, this graph in R shows clear seasonality patterns for weather across different seasons:
By decomposing the series, this might give visual cues as to whether seasonality exists among visits by individuals at particular times.
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
$endgroup$
$begingroup$
Thank you. I don't think this is a feasible solution because unless I misunderstood you I won't possibly be able to check each time series for a person visually if I have to to do it for hundreds of people.
$endgroup$
– maxachmed
Apr 5 at 15:43
$begingroup$
Fair point. While I would recommend examining seasonality in any case, one thing you could do is obtain the differences in visits across the dataset, i.e. 2 days, 3 days between each visit, etc. Then, plot these differences using a histogram - if you find that one particular time lag stands out then you could use this as a generalisation.
$endgroup$
– Michael Grogan
Apr 5 at 15:52
add a comment |
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
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
);
);
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%2fdatascience.stackexchange.com%2fquestions%2f48653%2fdetermining-if-time-series-follows-a-pattern%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
$begingroup$
It appears as though you are attempting to determine the presence of seasonality in this time series, i.e. a process that repeats itself at regular intervals.
In this regard, it might be a good idea to decompose your time series and check for the presence of seasonality trends.
As an example, this graph in R shows clear seasonality patterns for weather across different seasons:
By decomposing the series, this might give visual cues as to whether seasonality exists among visits by individuals at particular times.
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
$endgroup$
$begingroup$
Thank you. I don't think this is a feasible solution because unless I misunderstood you I won't possibly be able to check each time series for a person visually if I have to to do it for hundreds of people.
$endgroup$
– maxachmed
Apr 5 at 15:43
$begingroup$
Fair point. While I would recommend examining seasonality in any case, one thing you could do is obtain the differences in visits across the dataset, i.e. 2 days, 3 days between each visit, etc. Then, plot these differences using a histogram - if you find that one particular time lag stands out then you could use this as a generalisation.
$endgroup$
– Michael Grogan
Apr 5 at 15:52
add a comment |
$begingroup$
It appears as though you are attempting to determine the presence of seasonality in this time series, i.e. a process that repeats itself at regular intervals.
In this regard, it might be a good idea to decompose your time series and check for the presence of seasonality trends.
As an example, this graph in R shows clear seasonality patterns for weather across different seasons:
By decomposing the series, this might give visual cues as to whether seasonality exists among visits by individuals at particular times.
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
$endgroup$
$begingroup$
Thank you. I don't think this is a feasible solution because unless I misunderstood you I won't possibly be able to check each time series for a person visually if I have to to do it for hundreds of people.
$endgroup$
– maxachmed
Apr 5 at 15:43
$begingroup$
Fair point. While I would recommend examining seasonality in any case, one thing you could do is obtain the differences in visits across the dataset, i.e. 2 days, 3 days between each visit, etc. Then, plot these differences using a histogram - if you find that one particular time lag stands out then you could use this as a generalisation.
$endgroup$
– Michael Grogan
Apr 5 at 15:52
add a comment |
$begingroup$
It appears as though you are attempting to determine the presence of seasonality in this time series, i.e. a process that repeats itself at regular intervals.
In this regard, it might be a good idea to decompose your time series and check for the presence of seasonality trends.
As an example, this graph in R shows clear seasonality patterns for weather across different seasons:
By decomposing the series, this might give visual cues as to whether seasonality exists among visits by individuals at particular times.
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
$endgroup$
It appears as though you are attempting to determine the presence of seasonality in this time series, i.e. a process that repeats itself at regular intervals.
In this regard, it might be a good idea to decompose your time series and check for the presence of seasonality trends.
As an example, this graph in R shows clear seasonality patterns for weather across different seasons:
By decomposing the series, this might give visual cues as to whether seasonality exists among visits by individuals at particular times.
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
answered Apr 5 at 14:29
Michael GroganMichael Grogan
1964
1964
$begingroup$
Thank you. I don't think this is a feasible solution because unless I misunderstood you I won't possibly be able to check each time series for a person visually if I have to to do it for hundreds of people.
$endgroup$
– maxachmed
Apr 5 at 15:43
$begingroup$
Fair point. While I would recommend examining seasonality in any case, one thing you could do is obtain the differences in visits across the dataset, i.e. 2 days, 3 days between each visit, etc. Then, plot these differences using a histogram - if you find that one particular time lag stands out then you could use this as a generalisation.
$endgroup$
– Michael Grogan
Apr 5 at 15:52
add a comment |
$begingroup$
Thank you. I don't think this is a feasible solution because unless I misunderstood you I won't possibly be able to check each time series for a person visually if I have to to do it for hundreds of people.
$endgroup$
– maxachmed
Apr 5 at 15:43
$begingroup$
Fair point. While I would recommend examining seasonality in any case, one thing you could do is obtain the differences in visits across the dataset, i.e. 2 days, 3 days between each visit, etc. Then, plot these differences using a histogram - if you find that one particular time lag stands out then you could use this as a generalisation.
$endgroup$
– Michael Grogan
Apr 5 at 15:52
$begingroup$
Thank you. I don't think this is a feasible solution because unless I misunderstood you I won't possibly be able to check each time series for a person visually if I have to to do it for hundreds of people.
$endgroup$
– maxachmed
Apr 5 at 15:43
$begingroup$
Thank you. I don't think this is a feasible solution because unless I misunderstood you I won't possibly be able to check each time series for a person visually if I have to to do it for hundreds of people.
$endgroup$
– maxachmed
Apr 5 at 15:43
$begingroup$
Fair point. While I would recommend examining seasonality in any case, one thing you could do is obtain the differences in visits across the dataset, i.e. 2 days, 3 days between each visit, etc. Then, plot these differences using a histogram - if you find that one particular time lag stands out then you could use this as a generalisation.
$endgroup$
– Michael Grogan
Apr 5 at 15:52
$begingroup$
Fair point. While I would recommend examining seasonality in any case, one thing you could do is obtain the differences in visits across the dataset, i.e. 2 days, 3 days between each visit, etc. Then, plot these differences using a histogram - if you find that one particular time lag stands out then you could use this as a generalisation.
$endgroup$
– Michael Grogan
Apr 5 at 15:52
add a comment |
Thanks for contributing an answer to Data Science Stack Exchange!
- 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%2fdatascience.stackexchange.com%2fquestions%2f48653%2fdetermining-if-time-series-follows-a-pattern%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
