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Suppose $f_1,\ f_2,\ ...\ f_{n-1},\ f_n$ are $n$ non-stationary discrete signals of same length and linearly independent. Each $f_i$ is an intrinsic mode function (IMF) of a seismic signal evaluated by empirical mode decomposition (EMD). That is, they are amplitude-modulated, frequency-modulated signals. Linear combination of these signals can be expressed as follow:

$$F(t) = \sum_{j=1}^{j=n} \alpha _j f_j(t)$$

where, $\alpha_js$ are real numbers and they are to be selected such that $\sum_{j=0}^{j=n} \alpha_j\ne0$. Is it true to claim that $F(t)$ is also a non-stationary signal?

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No, try with $\alpha_j = 0$, $\forall j$.

I suspect this to be a tricky question, to deceive fast thinking. For instance, having the "same length" does not mean a lot, for a continuous variable $t$. Misleading fake clues to make the question look serious.

Without more hypotheses on the notion of stationarity (in law, wide-sense) and the statistical interrelations between the so-called signals (it would be wiser to talk about processes). See Marcus' answer for more examples.

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  • $\begingroup$ Let me to clarify the question. Suppose $f_1, ..., f_n$ be $n$ non-stationary discrete signals of same length and linearly independent. Also, $\alpha_js$ are to be selected such that $\sum_{j=1}^{j=n}\alpha_j\ne0$. With these conditions, what is the answer of my question? $\endgroup$
    – Pirooz
    May 18 '20 at 6:12
  • $\begingroup$ Do you have more hypotheses about the type of non-stationary in $f_i$? $\endgroup$ May 18 '20 at 6:40
  • $\begingroup$ Each $f_i$ is an intrinsic mode function (IMF) of a seismic signal evaluated by empirical mode decomposition technique (EMD, developed by Prof. Huang). That is, they are amplitude-modulated, frequency-modulated signals. $\endgroup$
    – Pirooz
    May 18 '20 at 12:10
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And to extend Laurent's excellent, since on-point, answer:

non-stationarity doesn't say anything about correlation.

For example, a totally valid scenario would be:

$$f_{j} = -f_{j-1}\,\text{for } j>1$$

and then, for any even $n$ and a lot of potential sequences $(\alpha_i)$, the result is a constant, too.

Whenever you see combinations of multiple stochastic entities, you should intuitively start wondering about correlations.

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  • $\begingroup$ Great addition, I answered a bit too fast before lunch $\endgroup$ May 17 '20 at 13:23
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    $\begingroup$ no, don't think so, the question was "is A?" and you proved by counter example that NOT A is true. $\endgroup$ May 17 '20 at 13:24
  • $\begingroup$ While you were answering, I added some bits about unstated hypotheses $\endgroup$ May 17 '20 at 13:24
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    $\begingroup$ can't double-upvote your answer :) $\endgroup$ May 17 '20 at 13:25
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    $\begingroup$ In econometrics, when the linear combination of multiple non-stationary series is stationary, the series are said to be cointegrated. Google for "johansen's cointegration methdology" if interested. In the two series case, cointegration is what engle and granger received the nobel prize in econometrics for. johansen later extended the concept to more than two series. $\endgroup$
    – mark leeds
    May 17 '20 at 14:06

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