Mixing
What's the point of time series analysis?
Clash Royale CLAN TAG#URR8PPP
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty margin-bottom:0;
up vote
1
down vote
favorite
What is the point of time series analysis?
There are plenty of other statistical methods, such as regression and machine learning, that have obvious use cases: regression can provide information on the relationship between two variables, while machine learning is great for prediction.
But meanwhile, I don't see what time series analysis is good for. Sure, I can fit an ARIMA model and use it for prediction, but what good is that when the confidence intervals for that prediction are going to be huge? There's a reason nobody can predict the stock market despite it being the most data-driven industry in world history.
Likewise, how do I use it to understand my process further? Sure, I can plot the ACF and go "aha! there's some dependence!", but then what? What's the point? Of course there's dependence, that's why you are doing time series analysis to begin with. You already knew there was dependence. But what are you going to use it for?
time-series
asked 2 hours ago
Dhalsim
91
New contributor
Dhalsim 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 |Â
up vote
1
down vote
favorite
What is the point of time series analysis?
There are plenty of other statistical methods, such as regression and machine learning, that have obvious use cases: regression can provide information on the relationship between two variables, while machine learning is great for prediction.
But meanwhile, I don't see what time series analysis is good for. Sure, I can fit an ARIMA model and use it for prediction, but what good is that when the confidence intervals for that prediction are going to be huge? There's a reason nobody can predict the stock market despite it being the most data-driven industry in world history.
Likewise, how do I use it to understand my process further? Sure, I can plot the ACF and go "aha! there's some dependence!", but then what? What's the point? Of course there's dependence, that's why you are doing time series analysis to begin with. You already knew there was dependence. But what are you going to use it for?
time-series
asked 2 hours ago
Dhalsim
91
New contributor
Dhalsim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
2
There are other use cases apart from finance and economics where they work fine.
– user2974951
2 hours ago
2
You cant predict stock market using other statistical & machine learning methods either, does this make them useless as well..?
– Tim♦
1 hour ago
add a comment |Â
up vote
1
down vote
favorite
up vote
1
down vote
favorite
What is the point of time series analysis?
There are plenty of other statistical methods, such as regression and machine learning, that have obvious use cases: regression can provide information on the relationship between two variables, while machine learning is great for prediction.
But meanwhile, I don't see what time series analysis is good for. Sure, I can fit an ARIMA model and use it for prediction, but what good is that when the confidence intervals for that prediction are going to be huge? There's a reason nobody can predict the stock market despite it being the most data-driven industry in world history.
Likewise, how do I use it to understand my process further? Sure, I can plot the ACF and go "aha! there's some dependence!", but then what? What's the point? Of course there's dependence, that's why you are doing time series analysis to begin with. You already knew there was dependence. But what are you going to use it for?
time-series
asked 2 hours ago
Dhalsim
91
New contributor
Dhalsim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
What is the point of time series analysis?
There are plenty of other statistical methods, such as regression and machine learning, that have obvious use cases: regression can provide information on the relationship between two variables, while machine learning is great for prediction.
But meanwhile, I don't see what time series analysis is good for. Sure, I can fit an ARIMA model and use it for prediction, but what good is that when the confidence intervals for that prediction are going to be huge? There's a reason nobody can predict the stock market despite it being the most data-driven industry in world history.
Likewise, how do I use it to understand my process further? Sure, I can plot the ACF and go "aha! there's some dependence!", but then what? What's the point? Of course there's dependence, that's why you are doing time series analysis to begin with. You already knew there was dependence. But what are you going to use it for?
time-series
time-series
asked 2 hours ago
Dhalsim
91
New contributor
Dhalsim 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
Dhalsim
91
New contributor
Dhalsim 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
Dhalsim
91
New contributor
Dhalsim 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
Dhalsim
91
asked 2 hours ago
Dhalsim
91
91
New contributor
Dhalsim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Dhalsim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Dhalsim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
2
There are other use cases apart from finance and economics where they work fine.
– user2974951
2 hours ago
2
You cant predict stock market using other statistical & machine learning methods either, does this make them useless as well..?
– Tim♦
1 hour ago
add a comment |Â
2
There are other use cases apart from finance and economics where they work fine.
– user2974951
2 hours ago
2
You cant predict stock market using other statistical & machine learning methods either, does this make them useless as well..?
– Tim♦
1 hour ago
2
2
There are other use cases apart from finance and economics where they work fine.
– user2974951
2 hours ago
There are other use cases apart from finance and economics where they work fine.
– user2974951
2 hours ago
2
2
You cant predict stock market using other statistical & machine learning methods either, does this make them useless as well..?
– Tim♦
1 hour ago
You cant predict stock market using other statistical & machine learning methods either, does this make them useless as well..?
– Tim♦
1 hour ago
add a comment |Â
2 Answers
2
active
oldest
votes
up vote
3
down vote
One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but not too much. There is money in this.
Other forecasting use cases are given in publications like the International Journal of Forecasting or Foresight. (Full disclosure: I'm an Associate Editor of Foresight.)
Yes, sometimes the prediction-intervals are huge. (I assume you mean PIs, not confidence-intervals. There is a difference.) This simply means that the process is hard to forecast. Then you need to mitigate. In forecasting supermarket sales, this means you need a lot of safety stock. In forecasting sea level rises, this means you need to build higher levees. I would say that a large prediction interval does provide useful information.
And for all forecasting use cases, time-series analyis is useful, though forecasting is a larger topic. You can often improve forecasts by taking the dependencies in your time series into account, so you need to understand them through analysis, which is more specific than just knowing dependencies are there.
Plus, people are interested in time series even if they do not forecast. Econometricians like to detect change points in macroeconomic time series. Or assess the impact of an intervention, such as a change in tax laws, on GDP or something else. You may want to skim through your favorite econometrics journal for more inspiration.
answered 2 hours ago
Stephan Kolassa
40.9k687151
add a comment |Â
up vote
1
down vote
Time series analysis can also contribute to effective anomaly or outlier detection in temporal data.
As an example, it is possible to fit an ARIMA model and calculate a forecast interval. Depending on the use case, the interval can be used to set a threshold, within which the process can be said to be in control; if new data falls outside the threshold it is flagged for further attention.
This blog post has a brief and broad overview of time series analysis for outlier detection. For a more in-depth treatment, researchers at ebay explain how they carried out anomaly detection at scale based on the statistical analysis of time series data.
answered 1 hour ago
redhqs
1112
New contributor
redhqs 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 |Â
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
3
down vote
One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but not too much. There is money in this.
Other forecasting use cases are given in publications like the International Journal of Forecasting or Foresight. (Full disclosure: I'm an Associate Editor of Foresight.)
Yes, sometimes the prediction-intervals are huge. (I assume you mean PIs, not confidence-intervals. There is a difference.) This simply means that the process is hard to forecast. Then you need to mitigate. In forecasting supermarket sales, this means you need a lot of safety stock. In forecasting sea level rises, this means you need to build higher levees. I would say that a large prediction interval does provide useful information.
And for all forecasting use cases, time-series analyis is useful, though forecasting is a larger topic. You can often improve forecasts by taking the dependencies in your time series into account, so you need to understand them through analysis, which is more specific than just knowing dependencies are there.
Plus, people are interested in time series even if they do not forecast. Econometricians like to detect change points in macroeconomic time series. Or assess the impact of an intervention, such as a change in tax laws, on GDP or something else. You may want to skim through your favorite econometrics journal for more inspiration.
answered 2 hours ago
Stephan Kolassa
40.9k687151
add a comment |Â
up vote
3
down vote
One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but not too much. There is money in this.
Other forecasting use cases are given in publications like the International Journal of Forecasting or Foresight. (Full disclosure: I'm an Associate Editor of Foresight.)
Yes, sometimes the prediction-intervals are huge. (I assume you mean PIs, not confidence-intervals. There is a difference.) This simply means that the process is hard to forecast. Then you need to mitigate. In forecasting supermarket sales, this means you need a lot of safety stock. In forecasting sea level rises, this means you need to build higher levees. I would say that a large prediction interval does provide useful information.
And for all forecasting use cases, time-series analyis is useful, though forecasting is a larger topic. You can often improve forecasts by taking the dependencies in your time series into account, so you need to understand them through analysis, which is more specific than just knowing dependencies are there.
Plus, people are interested in time series even if they do not forecast. Econometricians like to detect change points in macroeconomic time series. Or assess the impact of an intervention, such as a change in tax laws, on GDP or something else. You may want to skim through your favorite econometrics journal for more inspiration.
answered 2 hours ago
Stephan Kolassa
40.9k687151
add a comment |Â
up vote
3
down vote
up vote
3
down vote
One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but not too much. There is money in this.
Other forecasting use cases are given in publications like the International Journal of Forecasting or Foresight. (Full disclosure: I'm an Associate Editor of Foresight.)
Yes, sometimes the prediction-intervals are huge. (I assume you mean PIs, not confidence-intervals. There is a difference.) This simply means that the process is hard to forecast. Then you need to mitigate. In forecasting supermarket sales, this means you need a lot of safety stock. In forecasting sea level rises, this means you need to build higher levees. I would say that a large prediction interval does provide useful information.
And for all forecasting use cases, time-series analyis is useful, though forecasting is a larger topic. You can often improve forecasts by taking the dependencies in your time series into account, so you need to understand them through analysis, which is more specific than just knowing dependencies are there.
Plus, people are interested in time series even if they do not forecast. Econometricians like to detect change points in macroeconomic time series. Or assess the impact of an intervention, such as a change in tax laws, on GDP or something else. You may want to skim through your favorite econometrics journal for more inspiration.
answered 2 hours ago
Stephan Kolassa
40.9k687151
One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but not too much. There is money in this.
Other forecasting use cases are given in publications like the International Journal of Forecasting or Foresight. (Full disclosure: I'm an Associate Editor of Foresight.)
Yes, sometimes the prediction-intervals are huge. (I assume you mean PIs, not confidence-intervals. There is a difference.) This simply means that the process is hard to forecast. Then you need to mitigate. In forecasting supermarket sales, this means you need a lot of safety stock. In forecasting sea level rises, this means you need to build higher levees. I would say that a large prediction interval does provide useful information.
And for all forecasting use cases, time-series analyis is useful, though forecasting is a larger topic. You can often improve forecasts by taking the dependencies in your time series into account, so you need to understand them through analysis, which is more specific than just knowing dependencies are there.
Plus, people are interested in time series even if they do not forecast. Econometricians like to detect change points in macroeconomic time series. Or assess the impact of an intervention, such as a change in tax laws, on GDP or something else. You may want to skim through your favorite econometrics journal for more inspiration.
answered 2 hours ago
Stephan Kolassa
40.9k687151
answered 2 hours ago
Stephan Kolassa
40.9k687151
answered 2 hours ago
Stephan Kolassa
40.9k687151
answered 2 hours ago
Stephan Kolassa
40.9k687151
40.9k687151
add a comment |Â
add a comment |Â
up vote
1
down vote
Time series analysis can also contribute to effective anomaly or outlier detection in temporal data.
As an example, it is possible to fit an ARIMA model and calculate a forecast interval. Depending on the use case, the interval can be used to set a threshold, within which the process can be said to be in control; if new data falls outside the threshold it is flagged for further attention.
This blog post has a brief and broad overview of time series analysis for outlier detection. For a more in-depth treatment, researchers at ebay explain how they carried out anomaly detection at scale based on the statistical analysis of time series data.
answered 1 hour ago
redhqs
1112
New contributor
redhqs 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 |Â
up vote
1
down vote
Time series analysis can also contribute to effective anomaly or outlier detection in temporal data.
As an example, it is possible to fit an ARIMA model and calculate a forecast interval. Depending on the use case, the interval can be used to set a threshold, within which the process can be said to be in control; if new data falls outside the threshold it is flagged for further attention.
This blog post has a brief and broad overview of time series analysis for outlier detection. For a more in-depth treatment, researchers at ebay explain how they carried out anomaly detection at scale based on the statistical analysis of time series data.
answered 1 hour ago
redhqs
1112
New contributor
redhqs 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 |Â
up vote
1
down vote
up vote
1
down vote
Time series analysis can also contribute to effective anomaly or outlier detection in temporal data.
As an example, it is possible to fit an ARIMA model and calculate a forecast interval. Depending on the use case, the interval can be used to set a threshold, within which the process can be said to be in control; if new data falls outside the threshold it is flagged for further attention.
This blog post has a brief and broad overview of time series analysis for outlier detection. For a more in-depth treatment, researchers at ebay explain how they carried out anomaly detection at scale based on the statistical analysis of time series data.
answered 1 hour ago
redhqs
1112
New contributor
redhqs is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Time series analysis can also contribute to effective anomaly or outlier detection in temporal data.
As an example, it is possible to fit an ARIMA model and calculate a forecast interval. Depending on the use case, the interval can be used to set a threshold, within which the process can be said to be in control; if new data falls outside the threshold it is flagged for further attention.
This blog post has a brief and broad overview of time series analysis for outlier detection. For a more in-depth treatment, researchers at ebay explain how they carried out anomaly detection at scale based on the statistical analysis of time series data.
answered 1 hour ago
redhqs
1112
New contributor
redhqs is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 1 hour ago
redhqs
1112
New contributor
redhqs is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 1 hour ago
redhqs
1112
answered 1 hour ago
redhqs
1112
1112
New contributor
redhqs is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
redhqs is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
redhqs 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 |Â
add a comment |Â
Dhalsim is a new contributor. Be nice, and check out our Code of Conduct.
Dhalsim is a new contributor. Be nice, and check out our Code of Conduct.
Dhalsim is a new contributor. Be nice, and check out our Code of Conduct.
Dhalsim 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
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f367590%2fwhats-the-point-of-time-series-analysis%23new-answer', 'question_page');
);
Post as a guest
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
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
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
2
There are other use cases apart from finance and economics where they work fine.
– user2974951
2 hours ago
2
You cant predict stock market using other statistical & machine learning methods either, does this make them useless as well..?
– Tim♦
1 hour ago