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0answers
8 views

ACF and PACF Confidence Levels for ARMA

I'm trying to figure out where exactly to draw the confidence levels for the autocorrleation function (ACF) and the partial autocorrelation function (PACF) for an ARMA model. For PACF I found that a ...
0
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0answers
35 views

Moving average on the frequency domain - (Welch's Method?)

Is there a name for a filter that is using a moving average on the frequency domain? I am searching for such a filter to filter out long "pads" (synths) from music. Do any of you know about such ...
3
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4answers
108 views

Is there an algorithm for second order filtering of data where the filter frequency is not known a priori?

It is well known that a moving average algorithm done in the time domain is equivalent to a filter with frequency response $\mathrm{sinc}(\omega\tau)$ where $\tau$ is the averaging time. (see this ...
2
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0answers
45 views

Deconvolution of non-stationary, 1-D signal?

I have a time series that has been measured after convolution with a moving average filter. Knowing the parameters of the moving average filter, is it possible to reconstruct/constrain the values of ...
0
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1answer
186 views

Exponential average with time constant of slow, fast and impulse

I have been studying about exponential average. There are enough explanations about this at Internet, but they do not explain about the time constant. I have one channel with a $T$ seconds time ...
3
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2answers
2k views

What is the cut-off frequency of a moving average filter?

I need to design a moving average filter that has a cut-off frequency of 7.8 Hz. I have used moving average filters before, but as far as I'm aware, the only parameter that can be fed in is the number ...
0
votes
1answer
353 views

How to determine the window size and weights in Weighted Moving Average (WMA), given desired cut-off frequency?

I am trying to smooth my discrete-time data points using the method of WMA. Currently, I am using n as the window size and the weight array, ...
2
votes
1answer
131 views

Gain function calculation (frequency response)

Define moving average process $y_t := 0.5 x_t + 0.5 x_{t-1}$ where $x_t := e^{i2 \pi t}$. Its frequency response is then: $$H(f) = 0.5 + 0.5 e^{-i2\pi f}$$ Recall that the frequency response in ...
1
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2answers
545 views

Weight vector of an exponential moving average?

With weight vector I mean the vector with weights that you have to multiply the observations in the window that slides over your data with so if you add those products together it returns the value of ...
3
votes
1answer
322 views

Equivalent 2D mask of moving-average

I have the moving-average mask as mask = [1 1 1; 1 1 1; 1 1 1]; and then I compute the convolution 3 times ...
4
votes
1answer
426 views

How can I smoothly interpolate between 2 position?

I've got a 1D signal (position of a servo motor over time) and I've extracted 'peaks'/'key' positions picking running average "local extrema" points. Below is are 2 plots from 2 servos and the white ...
2
votes
3answers
760 views

Averaging filters

If a signal undergoes repeated averaging (n times) with a filter (h) of size m. Is it possible to achieve the same averaging result with an averaging filter(H) of a larger size? h = {1/m, 1/m... m ...
8
votes
4answers
878 views

How should a moving average handle missing data points?

I'm writing a program that averages the user's weight across different days. I'm planning to use a 5-point moving-average (current day, two before and two after). Sometimes, a data point is missing ...
7
votes
1answer
208 views

“Ensemble averaging … cannot track dynamic changes”?

A book claims this as a motivation for introducing exponential averaging: A disadvantage of ensemble averaging is that the resulting estimate cannot track dynamic changes occurring in the observed ...
5
votes
1answer
928 views

How to decide whether to use AR or MA for smoothing data?

Imagine I've got some offline data that I want to smooth. I could use an auto-regressive or moving-average filter of some appropriate order for conducting the smoothing. On which criteria should I ...