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From: Dan on 11 May 2010 10:08

I'm suggesting that when people spend a great deal of time focusing on one area they tend to become fixated on viewing other problems from that particular perspective. They then apply the rules that are valid in their world to another world where they have no justification. For example: The poster is (correctly) accustomed to pre-processing his fMRI data by smoothing it but this may be a grave error when analyzing stock data. Don't get me wrong... sometimes bringing in techniques from other disciplines is great but other-times you just waste time trying to fit the square peg where it doesn't belong.

Just my $0.02


Joe <chevysixfourredtobeexact(a)yahoo.com> wrote in message <a1a23e07-a7ca-4c6d-9755-c7b6aa108638(a)z15g2000prh.googlegroups.com>...
> On May 10, 11:49 am, "Dan" <sambe...(a)gmail.com> wrote:
> > When all you have is a hammer, everything looks like a nail.
> > I (humbly) suggest that you forget about potential fMRI/stock similarities and simply analyze the data.
> >
> > emceeaye <dumathechee...(a)yahoo.com> wrote in message <ab240ee4-f11f-486a-884e-58debbdbb...(a)23g2000pre.googlegroups.com>...
> > > On May 8, 4:16 am, Rune Allnor <all...(a)tele.ntnu.no> wrote:
> > > > On 8 Mai, 10:52, emceeaye <dumathechee...(a)yahoo.com> wrote:
> >
> > > > > Hi Matlab experts,
> > > > > My background is in functional magnetic resonance imaging time series
> > > > > data analysis, and I recently realized that there's no reason l can't
> > > > > also apply the same statistical procedures I use for my research to
> > > > > stock forecasting.
> >
> > > > Yes, there is.
> >
> > > > MRI is based on very specific physical relationships that can
> > > > be expressed compactly and conveniently by means of maths.
> >
> > > > Stocks indexes / prices are random series that rely on unpredictable,
> > > > unforeseen factors, including human psychological factors like fear,
> > > > nervousness and mass hysteria.
> >
> > > > Within the past couple of months Europe have experienced a couple
> > > > of factors that were unforeseeable, like the Iceland volcano
> > > > eruption and the Greek economical crisis. There is no way any
> > > > stock market model can include or account for such factors.
> >
> > > > Rune
> >
> > > Hi Rune,
> > > Part of what you say is true and part is not.  1) While stock prices
> > > are influenced by "unpredictable" and "unforseen" fluctuations in
> > > values such as those seen in the recent volatility in stock market
> > > prices and the other examples you gave, the prices certainly don't
> > > "RELY" on them as you stated--after all, they are the exceptions
> > > rather than the rules.
> >
> > > 2) Certainly evidence suggests that fMRI data is based on biological
> > > (or "physical" relationships) as you suggest, but there are a lot of
> > > unknowns about the physiological significance of the BOLD signal
> > > (e.g., is it blood flow, oxygen consumption, glucose consumption, a
> > > combination of all, or a consequence of magnetic field
> > > inhomogeneities, and the list goes on).  Furthermore, the signal
> > > changes being analyzed often do not reflect the underlying
> > > psychological/cognitive process that the researcher is intending to
> > > measure and the conclusions drawn and interpretations made from the
> > > results of analyzing this data are often leaps in logic and not
> > > necessarily accurate (happens all the time).  Also, Just as human
> > > "nervousness", and "self-consciousness" may influence stock prices,
> > > these same types of unpredictable, uncontrollable and unverifiable
> > > emotions/states and traits of the subjects may contaminate the sampled
> > > BOLD signal as well, which ultimately interferes with the
> > > intepretability of the data (happens all the time). so results of fMRI
> > > data may be expressed "conveniently" and "compactly" by math, but what
> > > is being deduced or extrapolated from the results is often far from
> > > "convenient" or "compact".
> >
> > > Routine techniques are often applied to fMRI BOLD data to reduce
> > > "noise" and "outliers" because they don't fit the expected behavior of
> > > the signal--why is this done? to eliminate the influence of anomalies
> > > and unexpected behavior in the signal being sampled (basically because
> > > the researcher cannot explain the reason for them and so discards the
> > > raw data for them). These same procedures of dealing with outliers and
> > > noise may also be applied to stock time series data to eliminate their
> > > influence on what the researcher is trying to determine by analyzing
> > > the data.  Does this mean that stock prices time series don't exhibit
> > > more unpredictable volatility than fMRI BOLD data?  Maybe or maybe
> > > not.  That is one of the questions I would like to answer.
> >
> > > One way to compensate for these unexpected fluctuations in the stock
> > > market is to increase the sampling period (e.g., from 1 year to 2, 3,
> > > 4 years worth of values)--by increasing the sampling period you reduce
> > > the impact of sudden unexpected fluctuations on the overall
> > > statistical results. Either way, I don't have to know much about
> > > econometrics to say that stock prices are certainly not "random" as
> > > you stated.  However, there are certainly many factors influencing the
> > > values and it's trying to account for as many of these factors as
> > > possible and determine their relative contribution to the results that
> > > may be a bit of a challenge.
> >
> > > emceeaye
>
> Not sure what your point is--First, I guess you're saying his
> knowledge of stock analysis is limited, and then you're suggesting to
> him in the same breath to analyze the data anyway?? Seems to me that
> if he has experience with fMRI time series, then he is prepared to
> deal with many of the challenges posed by that of stocks analysis. It
> must take one with only a hammer to know that everything looks like a
> nail.
From: Joe on 11 May 2010 19:00
On May 11, 7:08 am, "Dan" <sambe...(a)gmail.com> wrote:
> I'm suggesting that when people spend a great deal of time focusing on one area they tend to become fixated on viewing other problems from that particular perspective. They then apply the rules that are valid in their world to another world where they have no justification. For example: The poster is (correctly) accustomed to pre-processing his fMRI data by smoothing it but this may be a grave error when analyzing stock data. Don't get me wrong.... sometimes bringing in techniques from other disciplines is great but other-times you just waste time trying to fit the square peg where it doesn't belong.
>
> Just my $0.02
>
> Joe <chevysixfourredtobeex...(a)yahoo.com> wrote in message <a1a23e07-a7ca-4c6d-9755-c7b6aa108...(a)z15g2000prh.googlegroups.com>...
> > On May 10, 11:49 am, "Dan" <sambe...(a)gmail.com> wrote:
> > > When all you have is a hammer, everything looks like a nail.
> > > I (humbly) suggest that you forget about potential fMRI/stock similarities and simply analyze the data.
>
> > > emceeaye <dumathechee...(a)yahoo.com> wrote in message <ab240ee4-f11f-486a-884e-58debbdbb...(a)23g2000pre.googlegroups.com>...
> > > > On May 8, 4:16 am, Rune Allnor <all...(a)tele.ntnu.no> wrote:
> > > > > On 8 Mai, 10:52, emceeaye <dumathechee...(a)yahoo.com> wrote:
>
> > > > > > Hi Matlab experts,
> > > > > > My background is in functional magnetic resonance imaging time series
> > > > > > data analysis, and I recently realized that there's no reason l can't
> > > > > > also apply the same statistical procedures I use for my research to
> > > > > > stock forecasting.
>
> > > > > Yes, there is.
>
> > > > > MRI is based on very specific physical relationships that can
> > > > > be expressed compactly and conveniently by means of maths.
>
> > > > > Stocks indexes / prices are random series that rely on unpredictable,
> > > > > unforeseen factors, including human psychological factors like fear,
> > > > > nervousness and mass hysteria.
>
> > > > > Within the past couple of months Europe have experienced a couple
> > > > > of factors that were unforeseeable, like the Iceland volcano
> > > > > eruption and the Greek economical crisis. There is no way any
> > > > > stock market model can include or account for such factors.
>
> > > > > Rune
>
> > > > Hi Rune,
> > > > Part of what you say is true and part is not.  1) While stock prices
> > > > are influenced by "unpredictable" and "unforseen" fluctuations in
> > > > values such as those seen in the recent volatility in stock market
> > > > prices and the other examples you gave, the prices certainly don't
> > > > "RELY" on them as you stated--after all, they are the exceptions
> > > > rather than the rules.
>
> > > > 2) Certainly evidence suggests that fMRI data is based on biological
> > > > (or "physical" relationships) as you suggest, but there are a lot of
> > > > unknowns about the physiological significance of the BOLD signal
> > > > (e.g., is it blood flow, oxygen consumption, glucose consumption, a
> > > > combination of all, or a consequence of magnetic field
> > > > inhomogeneities, and the list goes on).  Furthermore, the signal
> > > > changes being analyzed often do not reflect the underlying
> > > > psychological/cognitive process that the researcher is intending to
> > > > measure and the conclusions drawn and interpretations made from the
> > > > results of analyzing this data are often leaps in logic and not
> > > > necessarily accurate (happens all the time).  Also, Just as human
> > > > "nervousness", and "self-consciousness" may influence stock prices,
> > > > these same types of unpredictable, uncontrollable and unverifiable
> > > > emotions/states and traits of the subjects may contaminate the sampled
> > > > BOLD signal as well, which ultimately interferes with the
> > > > intepretability of the data (happens all the time). so results of fMRI
> > > > data may be expressed "conveniently" and "compactly" by math, but what
> > > > is being deduced or extrapolated from the results is often far from
> > > > "convenient" or "compact".
>
> > > > Routine techniques are often applied to fMRI BOLD data to reduce
> > > > "noise" and "outliers" because they don't fit the expected behavior of
> > > > the signal--why is this done? to eliminate the influence of anomalies
> > > > and unexpected behavior in the signal being sampled (basically because
> > > > the researcher cannot explain the reason for them and so discards the
> > > > raw data for them). These same procedures of dealing with outliers and
> > > > noise may also be applied to stock time series data to eliminate their
> > > > influence on what the researcher is trying to determine by analyzing
> > > > the data.  Does this mean that stock prices time series don't exhibit
> > > > more unpredictable volatility than fMRI BOLD data?  Maybe or maybe
> > > > not.  That is one of the questions I would like to answer.
>
> > > > One way to compensate for these unexpected fluctuations in the stock
> > > > market is to increase the sampling period (e.g., from 1 year to 2, 3,
> > > > 4 years worth of values)--by increasing the sampling period you reduce
> > > > the impact of sudden unexpected fluctuations on the overall
> > > > statistical results. Either way, I don't have to know much about
> > > > econometrics to say that stock prices are certainly not "random" as
> > > > you stated.  However, there are certainly many factors influencing the
> > > > values and it's trying to account for as many of these factors as
> > > > possible and determine their relative contribution to the results that
> > > > may be a bit of a challenge.
>
> > > > emceeaye
>
> > Not sure what your point is--First, I guess you're saying his
> > knowledge of stock analysis is limited, and then you're suggesting to
> > him in the same breath to analyze the data anyway??  Seems to me that
> > if he has experience with fMRI time series, then he is prepared to
> > deal with many of the challenges posed by that of stocks analysis.  It
> > must take one with only a hammer to know that everything looks like a
> > nail.

To suggest that the poster is "applying the rules that are valid in
their world to another world where they have no justification" seems
to me to be a misappraisal of what he has stated.

Knowing nothing about fMRI analysis, by virtue of the fact that both
types of data are time series data, it's clear that by default there
will be similarities in statistical approach to both. As one of the
posters already pointed out, It's also clear that there are
differences between techniques used for time series analysis in the
two domains based on the differences in the nature of the data. Not
sure if you read the thread, but the original poster has already
acknowledged that there are clear differences and has identified this
as a target for further clarification. I don't think he said that
smoothing is a step he intends to use for stock time series data. t
looks like he mentioned several examples of fMRI pre-processing steps
in order to solicit clarification from others on whether they are
necessary for stock time series data, including "smoothing",
"detrending", "removing outliers", and fitting the data to a correct
model. It so happens that he is correct that detrending, removing
outliers, and looking for a correct model to accommodate the data, are
all necessary initial steps for analyzing stock time series data.

Just my thoughts.
From: Dan on 11 May 2010 22:53

OK.

Joe <chevysixfourredtobeexact(a)yahoo.com> wrote in message <cba10417-56aa-4bd3-9752-d864c544df28(a)p5g2000pri.googlegroups.com>...
> On May 11, 7:08 am, "Dan" <sambe...(a)gmail.com> wrote:
> > I'm suggesting that when people spend a great deal of time focusing on one area they tend to become fixated on viewing other problems from that particular perspective. They then apply the rules that are valid in their world to another world where they have no justification. For example: The poster is (correctly) accustomed to pre-processing his fMRI data by smoothing it but this may be a grave error when analyzing stock data. Don't get me wrong... sometimes bringing in techniques from other disciplines is great but other-times you just waste time trying to fit the square peg where it doesn't belong.
> >
> > Just my $0.02
> >
> > Joe <chevysixfourredtobeex...(a)yahoo.com> wrote in message <a1a23e07-a7ca-4c6d-9755-c7b6aa108...(a)z15g2000prh.googlegroups.com>...
> > > On May 10, 11:49 am, "Dan" <sambe...(a)gmail.com> wrote:
> > > > When all you have is a hammer, everything looks like a nail.
> > > > I (humbly) suggest that you forget about potential fMRI/stock similarities and simply analyze the data.
> >
> > > > emceeaye <dumathechee...(a)yahoo.com> wrote in message <ab240ee4-f11f-486a-884e-58debbdbb...(a)23g2000pre.googlegroups.com>...
> > > > > On May 8, 4:16 am, Rune Allnor <all...(a)tele.ntnu.no> wrote:
> > > > > > On 8 Mai, 10:52, emceeaye <dumathechee...(a)yahoo.com> wrote:
> >
> > > > > > > Hi Matlab experts,
> > > > > > > My background is in functional magnetic resonance imaging time series
> > > > > > > data analysis, and I recently realized that there's no reason l can't
> > > > > > > also apply the same statistical procedures I use for my research to
> > > > > > > stock forecasting.
> >
> > > > > > Yes, there is.
> >
> > > > > > MRI is based on very specific physical relationships that can
> > > > > > be expressed compactly and conveniently by means of maths.
> >
> > > > > > Stocks indexes / prices are random series that rely on unpredictable,
> > > > > > unforeseen factors, including human psychological factors like fear,
> > > > > > nervousness and mass hysteria.
> >
> > > > > > Within the past couple of months Europe have experienced a couple
> > > > > > of factors that were unforeseeable, like the Iceland volcano
> > > > > > eruption and the Greek economical crisis. There is no way any
> > > > > > stock market model can include or account for such factors.
> >
> > > > > > Rune
> >
> > > > > Hi Rune,
> > > > > Part of what you say is true and part is not.  1) While stock prices
> > > > > are influenced by "unpredictable" and "unforseen" fluctuations in
> > > > > values such as those seen in the recent volatility in stock market
> > > > > prices and the other examples you gave, the prices certainly don't
> > > > > "RELY" on them as you stated--after all, they are the exceptions
> > > > > rather than the rules.
> >
> > > > > 2) Certainly evidence suggests that fMRI data is based on biological
> > > > > (or "physical" relationships) as you suggest, but there are a lot of
> > > > > unknowns about the physiological significance of the BOLD signal
> > > > > (e.g., is it blood flow, oxygen consumption, glucose consumption, a
> > > > > combination of all, or a consequence of magnetic field
> > > > > inhomogeneities, and the list goes on).  Furthermore, the signal
> > > > > changes being analyzed often do not reflect the underlying
> > > > > psychological/cognitive process that the researcher is intending to
> > > > > measure and the conclusions drawn and interpretations made from the
> > > > > results of analyzing this data are often leaps in logic and not
> > > > > necessarily accurate (happens all the time).  Also, Just as human
> > > > > "nervousness", and "self-consciousness" may influence stock prices,
> > > > > these same types of unpredictable, uncontrollable and unverifiable
> > > > > emotions/states and traits of the subjects may contaminate the sampled
> > > > > BOLD signal as well, which ultimately interferes with the
> > > > > intepretability of the data (happens all the time). so results of fMRI
> > > > > data may be expressed "conveniently" and "compactly" by math, but what
> > > > > is being deduced or extrapolated from the results is often far from
> > > > > "convenient" or "compact".
> >
> > > > > Routine techniques are often applied to fMRI BOLD data to reduce
> > > > > "noise" and "outliers" because they don't fit the expected behavior of
> > > > > the signal--why is this done? to eliminate the influence of anomalies
> > > > > and unexpected behavior in the signal being sampled (basically because
> > > > > the researcher cannot explain the reason for them and so discards the
> > > > > raw data for them). These same procedures of dealing with outliers and
> > > > > noise may also be applied to stock time series data to eliminate their
> > > > > influence on what the researcher is trying to determine by analyzing
> > > > > the data.  Does this mean that stock prices time series don't exhibit
> > > > > more unpredictable volatility than fMRI BOLD data?  Maybe or maybe
> > > > > not.  That is one of the questions I would like to answer.
> >
> > > > > One way to compensate for these unexpected fluctuations in the stock
> > > > > market is to increase the sampling period (e.g., from 1 year to 2, 3,
> > > > > 4 years worth of values)--by increasing the sampling period you reduce
> > > > > the impact of sudden unexpected fluctuations on the overall
> > > > > statistical results. Either way, I don't have to know much about
> > > > > econometrics to say that stock prices are certainly not "random" as
> > > > > you stated.  However, there are certainly many factors influencing the
> > > > > values and it's trying to account for as many of these factors as
> > > > > possible and determine their relative contribution to the results that
> > > > > may be a bit of a challenge.
> >
> > > > > emceeaye
> >
> > > Not sure what your point is--First, I guess you're saying his
> > > knowledge of stock analysis is limited, and then you're suggesting to
> > > him in the same breath to analyze the data anyway??  Seems to me that
> > > if he has experience with fMRI time series, then he is prepared to
> > > deal with many of the challenges posed by that of stocks analysis.  It
> > > must take one with only a hammer to know that everything looks like a
> > > nail.
>
> To suggest that the poster is "applying the rules that are valid in
> their world to another world where they have no justification" seems
> to me to be a misappraisal of what he has stated.
>
> Knowing nothing about fMRI analysis, by virtue of the fact that both
> types of data are time series data, it's clear that by default there
> will be similarities in statistical approach to both. As one of the
> posters already pointed out, It's also clear that there are
> differences between techniques used for time series analysis in the
> two domains based on the differences in the nature of the data. Not
> sure if you read the thread, but the original poster has already
> acknowledged that there are clear differences and has identified this
> as a target for further clarification. I don't think he said that
> smoothing is a step he intends to use for stock time series data. t
> looks like he mentioned several examples of fMRI pre-processing steps
> in order to solicit clarification from others on whether they are
> necessary for stock time series data, including "smoothing",
> "detrending", "removing outliers", and fitting the data to a correct
> model. It so happens that he is correct that detrending, removing
> outliers, and looking for a correct model to accommodate the data, are
> all necessary initial steps for analyzing stock time series data.
>
> Just my thoughts.
From: Dan on 11 May 2010 22:58

Perhaps this problem is too vague and poorly defined.

Joe <chevysixfourredtobeexact(a)yahoo.com> wrote in message <cba10417-56aa-4bd3-9752-d864c544df28(a)p5g2000pri.googlegroups.com>...
> On May 11, 7:08 am, "Dan" <sambe...(a)gmail.com> wrote:

> > I'm suggesting that when people spend a great deal of time focusing on one area they tend to become fixated on viewing other problems from that particular perspective. They then apply the rules that are valid in their world to another world where they have no justification. For example: The poster is (correctly) accustomed to pre-processing his fMRI data by smoothing it but this may be a grave error when analyzing stock data. Don't get me wrong... sometimes bringing in techniques from other disciplines is great but other-times you just waste time trying to fit the square peg where it doesn't belong.
> >
> > Just my $0.02
> >
> > Joe <chevysixfourredtobeex...(a)yahoo.com> wrote in message <a1a23e07-a7ca-4c6d-9755-c7b6aa108...(a)z15g2000prh.googlegroups.com>...
> > > On May 10, 11:49 am, "Dan" <sambe...(a)gmail.com> wrote:
> > > > When all you have is a hammer, everything looks like a nail.
> > > > I (humbly) suggest that you forget about potential fMRI/stock similarities and simply analyze the data.
> >
> > > > emceeaye <dumathechee...(a)yahoo.com> wrote in message <ab240ee4-f11f-486a-884e-58debbdbb...(a)23g2000pre.googlegroups.com>...
> > > > > On May 8, 4:16 am, Rune Allnor <all...(a)tele.ntnu.no> wrote:
> > > > > > On 8 Mai, 10:52, emceeaye <dumathechee...(a)yahoo.com> wrote:
> >
> > > > > > > Hi Matlab experts,
> > > > > > > My background is in functional magnetic resonance imaging time series
> > > > > > > data analysis, and I recently realized that there's no reason l can't
> > > > > > > also apply the same statistical procedures I use for my research to
> > > > > > > stock forecasting.
> >
> > > > > > Yes, there is.
> >
> > > > > > MRI is based on very specific physical relationships that can
> > > > > > be expressed compactly and conveniently by means of maths.
> >
> > > > > > Stocks indexes / prices are random series that rely on unpredictable,
> > > > > > unforeseen factors, including human psychological factors like fear,
> > > > > > nervousness and mass hysteria.
> >
> > > > > > Within the past couple of months Europe have experienced a couple
> > > > > > of factors that were unforeseeable, like the Iceland volcano
> > > > > > eruption and the Greek economical crisis. There is no way any
> > > > > > stock market model can include or account for such factors.
> >
> > > > > > Rune
> >
> > > > > Hi Rune,
> > > > > Part of what you say is true and part is not.  1) While stock prices
> > > > > are influenced by "unpredictable" and "unforseen" fluctuations in
> > > > > values such as those seen in the recent volatility in stock market
> > > > > prices and the other examples you gave, the prices certainly don't
> > > > > "RELY" on them as you stated--after all, they are the exceptions
> > > > > rather than the rules.
> >
> > > > > 2) Certainly evidence suggests that fMRI data is based on biological
> > > > > (or "physical" relationships) as you suggest, but there are a lot of
> > > > > unknowns about the physiological significance of the BOLD signal
> > > > > (e.g., is it blood flow, oxygen consumption, glucose consumption, a
> > > > > combination of all, or a consequence of magnetic field
> > > > > inhomogeneities, and the list goes on).  Furthermore, the signal
> > > > > changes being analyzed often do not reflect the underlying
> > > > > psychological/cognitive process that the researcher is intending to
> > > > > measure and the conclusions drawn and interpretations made from the
> > > > > results of analyzing this data are often leaps in logic and not
> > > > > necessarily accurate (happens all the time).  Also, Just as human
> > > > > "nervousness", and "self-consciousness" may influence stock prices,
> > > > > these same types of unpredictable, uncontrollable and unverifiable
> > > > > emotions/states and traits of the subjects may contaminate the sampled
> > > > > BOLD signal as well, which ultimately interferes with the
> > > > > intepretability of the data (happens all the time). so results of fMRI
> > > > > data may be expressed "conveniently" and "compactly" by math, but what
> > > > > is being deduced or extrapolated from the results is often far from
> > > > > "convenient" or "compact".
> >
> > > > > Routine techniques are often applied to fMRI BOLD data to reduce
> > > > > "noise" and "outliers" because they don't fit the expected behavior of
> > > > > the signal--why is this done? to eliminate the influence of anomalies
> > > > > and unexpected behavior in the signal being sampled (basically because
> > > > > the researcher cannot explain the reason for them and so discards the
> > > > > raw data for them). These same procedures of dealing with outliers and
> > > > > noise may also be applied to stock time series data to eliminate their
> > > > > influence on what the researcher is trying to determine by analyzing
> > > > > the data.  Does this mean that stock prices time series don't exhibit
> > > > > more unpredictable volatility than fMRI BOLD data?  Maybe or maybe
> > > > > not.  That is one of the questions I would like to answer.
> >
> > > > > One way to compensate for these unexpected fluctuations in the stock
> > > > > market is to increase the sampling period (e.g., from 1 year to 2, 3,
> > > > > 4 years worth of values)--by increasing the sampling period you reduce
> > > > > the impact of sudden unexpected fluctuations on the overall
> > > > > statistical results. Either way, I don't have to know much about
> > > > > econometrics to say that stock prices are certainly not "random" as
> > > > > you stated.  However, there are certainly many factors influencing the
> > > > > values and it's trying to account for as many of these factors as
> > > > > possible and determine their relative contribution to the results that
> > > > > may be a bit of a challenge.
> >
> > > > > emceeaye
> >
> > > Not sure what your point is--First, I guess you're saying his
> > > knowledge of stock analysis is limited, and then you're suggesting to
> > > him in the same breath to analyze the data anyway??  Seems to me that
> > > if he has experience with fMRI time series, then he is prepared to
> > > deal with many of the challenges posed by that of stocks analysis.  It
> > > must take one with only a hammer to know that everything looks like a
> > > nail.
>
> To suggest that the poster is "applying the rules that are valid in
> their world to another world where they have no justification" seems
> to me to be a misappraisal of what he has stated.
>
> Knowing nothing about fMRI analysis, by virtue of the fact that both
> types of data are time series data, it's clear that by default there
> will be similarities in statistical approach to both. As one of the
> posters already pointed out, It's also clear that there are
> differences between techniques used for time series analysis in the
> two domains based on the differences in the nature of the data. Not
> sure if you read the thread, but the original poster has already
> acknowledged that there are clear differences and has identified this
> as a target for further clarification. I don't think he said that
> smoothing is a step he intends to use for stock time series data. t
> looks like he mentioned several examples of fMRI pre-processing steps
> in order to solicit clarification from others on whether they are
> necessary for stock time series data, including "smoothing",
> "detrending", "removing outliers", and fitting the data to a correct
> model. It so happens that he is correct that detrending, removing
> outliers, and looking for a correct model to accommodate the data, are
> all necessary initial steps for analyzing stock time series data.
>
> Just my thoughts.
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