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Results tagged with moving-average
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user 3382
In time series analysis, the moving-average (MA) model is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term.
6
votes
Accepted
Can a Moving Average be used as a dependent variable in a regression model?
The moving-average will be auto-correlated (even if the original series is not auto-correlated) thus this is a potential violation of the subsequent causal model. I would simply include the variable a …
- 30k
4
votes
What is the implication of unit root of MA?
If the roots of the MA process indicate a violation this can be due to a variety of causes;
Over-differencing of Y
Redundancy of the AR and MA structure
Omitted deterministic variables ( Pulses/Leve …
- 30k
4
votes
Difference between MA and AR
A finite AR model can be expressed as an MA model and vice-versa , If one has an ar(1) model with coefficient .333333333 then the models are (nearly) identical .
Consider the case for an ar(1) with co …
- 30k
2
votes
Accepted
Detecting anomalies in a time series where new data points will be continuously added
The suggsted approach is to use all of the data to form a reasonable XARMAX model which might be a simple weighted average of the past (ARMA model) OR perhaps a deterministic model(X) with possible le …
- 30k
2
votes
simple trend analysis, time series
To examine/test for an exceptional value , one needs to have an expectation for that value . Essentially one needs to compute the probability of what was observed before the observation took place. Th …
- 30k
1
vote
Should a moving average be applied to time series data before performing a linear regression?
No as you can incorporate lags of the output series and contemporary and lag structure for causal series while detecting the presence of latent deterministic structure. See http://www.autobox.com/pdfs …
- 30k
1
vote
What are different time-series types?
Both of the "models" that you listed are particular types of ARIMA models where one series exists and one wishes to use the history of the series to generate a forecast. Examples of this are here Fore …
- 30k
1
vote
Accepted
Possible issues using moving average as an input for exponential smoothing?
As you presented you are first applying filter1 ( equal weights ) ( 90 day average ) and then you are applying filter2 to analyze/convert/smooth those results.
This is akin to creating a cumulative s …
- 30k
1
vote
Accepted
How to compute error terms in moving average time series model?
" Are they just residuals? " . Yes !
Read my response to Moving-average model error terms
- 30k
1
vote
Similarity of time series
After one forms what might be regarded as a common model ( perhaps slightly over-specified) one could incorporate anomaly detection http://www.unc.edu/~jbhill/tsay.pdf to obtain robust model parameter …
- 30k
1
vote
Can white noise be removed using moving averages?
The role of the ARIMA structure is to convert colored noise to white noise. Smoothing out white noise would seem to be potentially creating colored noise. The concept of an oxymoron pops up in my head …
- 30k
1
vote
When to use AR and when to use MA model?
Generally , if the pure AR model has p coefficients AND the pure MA model has q coefficients select/use the AR model if p = < q OTHERWISE use the MA model.
- 30k
0
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What is the meaning of having the autocorelation function a cut of at a specific lag and at ...
It is an MA signature because the # of significant PAC is greater than the # of significant ACF i.e. the PAC dominates.
See http://autobox.com/cms/index.php/afs-university/autobox-examples/modeling-w …
- 30k
0
votes
Is having a high $p$ and $q$ evidence that ARIMA would be a better model than moving average?
a high p and a high q probably suggest either bad software or data that is heavily impacted by anthropomorphic effects which need to be identified. If you have such a data set .. post it and I will tr …
- 30k
0
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Moving Average (q) interpretation
Just take the ma polynomial (psi weights )and compute it's inverse. This will be the auto-projective polynomial ( pi weights) reflecting how previous values are weighted. In this way the model's memor …
- 30k