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Results tagged with arima
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user 238499
Refers to the AutoRegressive Integrated Moving Average model used in time series modeling both for data description and for forecasting. This model generalizes the ARMA model by including a term for differencing, which is useful for removing trends and handling some types of non-stationarity.
6
votes
Should my time series be stationary to use ARIMA model?
It should be stationary in order to use ARMA(p, q) (a short way of saying ARIMA(p, 0, q)). However, the general ARIMA model can handle nonstationary series as well. …
- 2,665
1
vote
ARMA and AR processes
Predictions of stationary ARMA models will quickly converge towards the mean of the series (treat the AR model as a particular case of an ARMA). Depending on how big $h$ is in your specific case, you …
- 2,665
1
vote
Should I analyze the residuals of a model selected by auto.arima?
Absolutely! If you want your work to be serious, you should never blindly trust the methods you use. This means analyzing the residuals but also other tests. Always check the validity of your models. …
- 2,665
1
vote
ARMAX for Bitcoin prediction via sentiment
Your model can potentially be useful. As a first approach, I think you can give it a try. What I would do is working with a time series of bitcoin prizes (or returns), not just "bitcoin prize at time …
- 2,665
1
vote
Accepted
How do I compare time series forecast models? (ARIMA vs HoltWinter)
Short answer: you can build a test set to validate time series models! If that weren't the case, and leaving a few of the final observations for testing then time series would be a pretty bad techniqu …
- 2,665
0
votes
Is statistical significance of a regressor important in forecasting different scenarios?
You can use "non-significant" regressors in the same way you use the significant ones. After all, significance is just an arbitrary choice.
What "non-significance" means is that maybe you should cons …
- 2,665
0
votes
Applying AIC to determine appropriate ARIMA model
If you already know that your series follows an ARIMA process, just pick the simplest model that leaves no significant autocorrelations on its residuals (you may also want to check the squared residuals …
- 2,665
0
votes
Accepted
Interpreting qq plot from ARIMA residuals
My interpretation would be that the middle values of the sample are close to what you'd expect from normally distributed data, as it follows the straight line from the diagram closely.
However, it se …
- 2,665