Analysis of Time Series data The Next CEO of Stack Overflow2019 Community Moderator ElectionTime series prediction using ARIMA vs LSTMTime Series Analysis in RForecasting non-negative sparse time-series dataForecasting one time series with missing data with help of other time seriesWhat are the prerequisites before running Holt Winters Model?Continuously predicting eventsTime series forecasting using multiple time series as training datademand forecast for B2BForecasting energy consumption with no historical data when there is a trendAre RNN or LSTM appropriate Neural Networks approaches for multivariate time-series regression?
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Analysis of Time Series data
The Next CEO of Stack Overflow2019 Community Moderator ElectionTime series prediction using ARIMA vs LSTMTime Series Analysis in RForecasting non-negative sparse time-series dataForecasting one time series with missing data with help of other time seriesWhat are the prerequisites before running Holt Winters Model?Continuously predicting eventsTime series forecasting using multiple time series as training datademand forecast for B2BForecasting energy consumption with no historical data when there is a trendAre RNN or LSTM appropriate Neural Networks approaches for multivariate time-series regression?
$begingroup$
The below graph is a scatterplot of daily stock price. My aim is to predict future stock price of the company.
From the scatterplot it seems that it is a multiplicative model, so I tried to "decompose" it in R. However it says that "time series has no or less than 2 periods". I also obtained a periodogram, which has only one peak at frequency close to 0.
However, my teacher told me that this time series cannot have a trend therefore to eliminate the seasonality I have to consider its period as 7 and then eliminate it choosing an appropriate model.
Can anyone tell me what could be an appropriate model along with a proper justification? Also is it true that the series cannot have a trend?
r time-series forecast data-analysis
asked Feb 20 at 17:06
Jor_ElJor_El
312
$endgroup$
add a comment |
$begingroup$
The below graph is a scatterplot of daily stock price. My aim is to predict future stock price of the company.
From the scatterplot it seems that it is a multiplicative model, so I tried to "decompose" it in R. However it says that "time series has no or less than 2 periods". I also obtained a periodogram, which has only one peak at frequency close to 0.
However, my teacher told me that this time series cannot have a trend therefore to eliminate the seasonality I have to consider its period as 7 and then eliminate it choosing an appropriate model.
Can anyone tell me what could be an appropriate model along with a proper justification? Also is it true that the series cannot have a trend?
r time-series forecast data-analysis
asked Feb 20 at 17:06
Jor_ElJor_El
312
$endgroup$
add a comment |
$begingroup$
The below graph is a scatterplot of daily stock price. My aim is to predict future stock price of the company.
From the scatterplot it seems that it is a multiplicative model, so I tried to "decompose" it in R. However it says that "time series has no or less than 2 periods". I also obtained a periodogram, which has only one peak at frequency close to 0.
However, my teacher told me that this time series cannot have a trend therefore to eliminate the seasonality I have to consider its period as 7 and then eliminate it choosing an appropriate model.
Can anyone tell me what could be an appropriate model along with a proper justification? Also is it true that the series cannot have a trend?
r time-series forecast data-analysis
asked Feb 20 at 17:06
Jor_ElJor_El
312
$endgroup$
The below graph is a scatterplot of daily stock price. My aim is to predict future stock price of the company.
From the scatterplot it seems that it is a multiplicative model, so I tried to "decompose" it in R. However it says that "time series has no or less than 2 periods". I also obtained a periodogram, which has only one peak at frequency close to 0.
However, my teacher told me that this time series cannot have a trend therefore to eliminate the seasonality I have to consider its period as 7 and then eliminate it choosing an appropriate model.
Can anyone tell me what could be an appropriate model along with a proper justification? Also is it true that the series cannot have a trend?
r time-series forecast data-analysis
r time-series forecast data-analysis
asked Feb 20 at 17:06
Jor_ElJor_El
312
asked Feb 20 at 17:06
Jor_ElJor_El
312
asked Feb 20 at 17:06
Jor_ElJor_El
312
asked Feb 20 at 17:06
Jor_ElJor_El
312
asked Feb 20 at 17:06
Jor_ElJor_El
312
312
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Have you tried taking the first difference? This amounts to taking the first derivative, and is generally a good way to de-trend a time series.
However, if you want to use seasonality, fit a regression model of form
$$
X_t = X_t-k + epsilon
$$
where $k$ is the number of time periods between seasons. For example, if you have monthly observations, using $k=12$ might make sense, as this removes the annual seasonality.
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
$endgroup$
$begingroup$
Isn't the above formula valid if time series is additive? Also how did you know that the given time series is additive?
$endgroup$
– Jor_El
Feb 21 at 9:29
$begingroup$
The above construct only helps remove the seasonality from $k$ time periods ago. If the time series does not have seasonality, the regression model should have very small coefficients. You can also use regularization to determine if the coefficients should be non-zero.
$endgroup$
– David Atlas
Feb 21 at 12:58
$begingroup$
What model(additive or multiplicative) do you think the above time series follows?Also, how would I check whether seasonality is present or not?
$endgroup$
– Jor_El
Feb 21 at 20:53
$begingroup$
See this link for more information on how to tell if a time series is additive or multiplicative:r-bloggers.com/is-my-time-series-additive-or-multiplicative My answer above shows how to test for seasonality. Simply use the p-values of the regression model to inform if significant seasonality is present.
$endgroup$
– David Atlas
Feb 21 at 21:17
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
Have you tried taking the first difference? This amounts to taking the first derivative, and is generally a good way to de-trend a time series.
However, if you want to use seasonality, fit a regression model of form
$$
X_t = X_t-k + epsilon
$$
where $k$ is the number of time periods between seasons. For example, if you have monthly observations, using $k=12$ might make sense, as this removes the annual seasonality.
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
$endgroup$
$begingroup$
Isn't the above formula valid if time series is additive? Also how did you know that the given time series is additive?
$endgroup$
– Jor_El
Feb 21 at 9:29
$begingroup$
The above construct only helps remove the seasonality from $k$ time periods ago. If the time series does not have seasonality, the regression model should have very small coefficients. You can also use regularization to determine if the coefficients should be non-zero.
$endgroup$
– David Atlas
Feb 21 at 12:58
$begingroup$
What model(additive or multiplicative) do you think the above time series follows?Also, how would I check whether seasonality is present or not?
$endgroup$
– Jor_El
Feb 21 at 20:53
$begingroup$
See this link for more information on how to tell if a time series is additive or multiplicative:r-bloggers.com/is-my-time-series-additive-or-multiplicative My answer above shows how to test for seasonality. Simply use the p-values of the regression model to inform if significant seasonality is present.
$endgroup$
– David Atlas
Feb 21 at 21:17
add a comment |
$begingroup$
Have you tried taking the first difference? This amounts to taking the first derivative, and is generally a good way to de-trend a time series.
However, if you want to use seasonality, fit a regression model of form
$$
X_t = X_t-k + epsilon
$$
where $k$ is the number of time periods between seasons. For example, if you have monthly observations, using $k=12$ might make sense, as this removes the annual seasonality.
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
$endgroup$
$begingroup$
Isn't the above formula valid if time series is additive? Also how did you know that the given time series is additive?
$endgroup$
– Jor_El
Feb 21 at 9:29
$begingroup$
The above construct only helps remove the seasonality from $k$ time periods ago. If the time series does not have seasonality, the regression model should have very small coefficients. You can also use regularization to determine if the coefficients should be non-zero.
$endgroup$
– David Atlas
Feb 21 at 12:58
$begingroup$
What model(additive or multiplicative) do you think the above time series follows?Also, how would I check whether seasonality is present or not?
$endgroup$
– Jor_El
Feb 21 at 20:53
$begingroup$
See this link for more information on how to tell if a time series is additive or multiplicative:r-bloggers.com/is-my-time-series-additive-or-multiplicative My answer above shows how to test for seasonality. Simply use the p-values of the regression model to inform if significant seasonality is present.
$endgroup$
– David Atlas
Feb 21 at 21:17
add a comment |
$begingroup$
Have you tried taking the first difference? This amounts to taking the first derivative, and is generally a good way to de-trend a time series.
However, if you want to use seasonality, fit a regression model of form
$$
X_t = X_t-k + epsilon
$$
where $k$ is the number of time periods between seasons. For example, if you have monthly observations, using $k=12$ might make sense, as this removes the annual seasonality.
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
$endgroup$
Have you tried taking the first difference? This amounts to taking the first derivative, and is generally a good way to de-trend a time series.
However, if you want to use seasonality, fit a regression model of form
$$
X_t = X_t-k + epsilon
$$
where $k$ is the number of time periods between seasons. For example, if you have monthly observations, using $k=12$ might make sense, as this removes the annual seasonality.
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
answered Feb 21 at 1:18
David AtlasDavid Atlas
312
312
$begingroup$
Isn't the above formula valid if time series is additive? Also how did you know that the given time series is additive?
$endgroup$
– Jor_El
Feb 21 at 9:29
$begingroup$
The above construct only helps remove the seasonality from $k$ time periods ago. If the time series does not have seasonality, the regression model should have very small coefficients. You can also use regularization to determine if the coefficients should be non-zero.
$endgroup$
– David Atlas
Feb 21 at 12:58
$begingroup$
What model(additive or multiplicative) do you think the above time series follows?Also, how would I check whether seasonality is present or not?
$endgroup$
– Jor_El
Feb 21 at 20:53
$begingroup$
See this link for more information on how to tell if a time series is additive or multiplicative:r-bloggers.com/is-my-time-series-additive-or-multiplicative My answer above shows how to test for seasonality. Simply use the p-values of the regression model to inform if significant seasonality is present.
$endgroup$
– David Atlas
Feb 21 at 21:17
add a comment |
$begingroup$
Isn't the above formula valid if time series is additive? Also how did you know that the given time series is additive?
$endgroup$
– Jor_El
Feb 21 at 9:29
$begingroup$
The above construct only helps remove the seasonality from $k$ time periods ago. If the time series does not have seasonality, the regression model should have very small coefficients. You can also use regularization to determine if the coefficients should be non-zero.
$endgroup$
– David Atlas
Feb 21 at 12:58
$begingroup$
What model(additive or multiplicative) do you think the above time series follows?Also, how would I check whether seasonality is present or not?
$endgroup$
– Jor_El
Feb 21 at 20:53
$begingroup$
See this link for more information on how to tell if a time series is additive or multiplicative:r-bloggers.com/is-my-time-series-additive-or-multiplicative My answer above shows how to test for seasonality. Simply use the p-values of the regression model to inform if significant seasonality is present.
$endgroup$
– David Atlas
Feb 21 at 21:17
$begingroup$
Isn't the above formula valid if time series is additive? Also how did you know that the given time series is additive?
$endgroup$
– Jor_El
Feb 21 at 9:29
$begingroup$
Isn't the above formula valid if time series is additive? Also how did you know that the given time series is additive?
$endgroup$
– Jor_El
Feb 21 at 9:29
$begingroup$
The above construct only helps remove the seasonality from $k$ time periods ago. If the time series does not have seasonality, the regression model should have very small coefficients. You can also use regularization to determine if the coefficients should be non-zero.
$endgroup$
– David Atlas
Feb 21 at 12:58
$begingroup$
The above construct only helps remove the seasonality from $k$ time periods ago. If the time series does not have seasonality, the regression model should have very small coefficients. You can also use regularization to determine if the coefficients should be non-zero.
$endgroup$
– David Atlas
Feb 21 at 12:58
$begingroup$
What model(additive or multiplicative) do you think the above time series follows?Also, how would I check whether seasonality is present or not?
$endgroup$
– Jor_El
Feb 21 at 20:53
$begingroup$
What model(additive or multiplicative) do you think the above time series follows?Also, how would I check whether seasonality is present or not?
$endgroup$
– Jor_El
Feb 21 at 20:53
$begingroup$
See this link for more information on how to tell if a time series is additive or multiplicative:r-bloggers.com/is-my-time-series-additive-or-multiplicative My answer above shows how to test for seasonality. Simply use the p-values of the regression model to inform if significant seasonality is present.
$endgroup$
– David Atlas
Feb 21 at 21:17
$begingroup$
See this link for more information on how to tell if a time series is additive or multiplicative:r-bloggers.com/is-my-time-series-additive-or-multiplicative My answer above shows how to test for seasonality. Simply use the p-values of the regression model to inform if significant seasonality is present.
$endgroup$
– David Atlas
Feb 21 at 21:17
add a comment |
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