On Fri, Oct 14, 2011 at 2:59 PM, wrote:
> On Fri, Oct 14, 2011 at 2:29 PM, wrote:
>> On Fri, Oct 14, 2011 at 2:18 PM, Alan G Isaac wrote:
>>> On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote:
If I remember correctly, signal.lfilter doesn't require stationarity,
but handling of the s
On Fri, Oct 14, 2011 at 2:29 PM, wrote:
> On Fri, Oct 14, 2011 at 2:18 PM, Alan G Isaac wrote:
>> On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote:
>>> If I remember correctly, signal.lfilter doesn't require stationarity,
>>> but handling of the starting values is a bit difficult.
>>
>>
>> Hmm.
On Fri, Oct 14, 2011 at 2:39 PM, Skipper Seabold wrote:
> On Fri, Oct 14, 2011 at 2:18 PM, Alan G Isaac wrote:
>>
>> On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote:
>> > If I remember correctly, signal.lfilter doesn't require stationarity,
>> > but handling of the starting values is a bit diff
On Fri, Oct 14, 2011 at 2:18 PM, Alan G Isaac wrote:
>
> On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote:
> > If I remember correctly, signal.lfilter doesn't require stationarity,
> > but handling of the starting values is a bit difficult.
>
>
> Hmm. Yes.
> AR(1) is trivial, but how do you hand
On Fri, Oct 14, 2011 at 2:18 PM, Alan G Isaac wrote:
> On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote:
>> If I remember correctly, signal.lfilter doesn't require stationarity,
>> but handling of the starting values is a bit difficult.
>
>
> Hmm. Yes.
> AR(1) is trivial, but how do you handle h
On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote:
> If I remember correctly, signal.lfilter doesn't require stationarity,
> but handling of the starting values is a bit difficult.
Hmm. Yes.
AR(1) is trivial, but how do you handle higher orders?
Thanks,
Alan
___
On Fri, Oct 14, 2011 at 1:26 PM, Alan G Isaac wrote:
>>> Assuming stationarity ...
>
> On 10/14/2011 1:22 PM, josef.p...@gmail.com wrote:
>> maybe ?
>
> I just meant that the MA approximation is
> not reliable for a non-stationary AR.
> E.g., http://www.jstor.org/stable/2348631
section 5: simulat
>> Assuming stationarity ...
On 10/14/2011 1:22 PM, josef.p...@gmail.com wrote:
> maybe ?
I just meant that the MA approximation is
not reliable for a non-stationary AR.
E.g., http://www.jstor.org/stable/2348631
Cheers,
Alan
___
NumPy-Discussion maili
On Fri, Oct 14, 2011 at 12:49 PM, Alan G Isaac wrote:
> On 10/14/2011 12:21 PM, josef.p...@gmail.com wrote:
>> One other way to simulate the AR is to get the (truncated)
>> MA-representation, and then convolve can be used
>
>
> Assuming stationarity ...
maybe ?
If it's integrated, then you need a
On 10/14/2011 12:21 PM, josef.p...@gmail.com wrote:
> One other way to simulate the AR is to get the (truncated)
> MA-representation, and then convolve can be used
Assuming stationarity ...
Alan
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy
On Fri, Oct 14, 2011 at 11:56 AM, Fabrice Silva wrote:
> Le vendredi 14 octobre 2011 à 10:49 -0400, josef.p...@gmail.com a
> écrit :
>> On Fri, Oct 14, 2011 at 10:24 AM, Alan G Isaac wrote:
>> > As a simple example, if I have y0 and a white noise series e,
>> > what is the best way to produces a
Le vendredi 14 octobre 2011 à 10:49 -0400, josef.p...@gmail.com a
écrit :
> On Fri, Oct 14, 2011 at 10:24 AM, Alan G Isaac wrote:
> > As a simple example, if I have y0 and a white noise series e,
> > what is the best way to produces a series y such that y[t] = 0.9*y[t-1] +
> > e[t]
> > for t=1,2,
On Fri, Oct 14, 2011 at 10:24 AM, Alan G Isaac wrote:
> As a simple example, if I have y0 and a white noise series e,
> what is the best way to produces a series y such that y[t] = 0.9*y[t-1] + e[t]
> for t=1,2,...?
>
> 1. How can I best simulate an autoregressive process using NumPy?
>
> 2. With
As a simple example, if I have y0 and a white noise series e,
what is the best way to produces a series y such that y[t] = 0.9*y[t-1] + e[t]
for t=1,2,...?
1. How can I best simulate an autoregressive process using NumPy?
2. With SciPy, it looks like I could do this as
e[0] = y0
signal.lfilter((1
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