1:07 p.m.
To better understand your model, could you give us an idea of the
independent variable you are using to predict NEPOOL prices? Typical
independent variables include day of week/holiday dummy variables,
natural gas price, heating oil price, weather variables, etc..
Thanks
- Mike
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Message: 1
Date: Wed, 30 Sep 2009 19:19:58 +0500
From: Saqib Ilyas <msaqib(a)gmail.com>
Subject: [Gretl-users] GARCH for day-ahead electricity prices
To: gretl-users(a)lists.wfu.edu
Message-ID:
<262b67200909300719k16ab8e46m5e63bd83bce25ddf(a)mail.gmail.com>
Content-Type: text/plain; charset="iso-8859-1"
Hi all
I am using the 2006 till 2009 data for the New England pool of day-ahead
weighted average prices. I am not very fluent with econometrics, but I
read
in some papers that GARCH has been used successfully to forecast
electricity
wholesale prices. When I train a GARCH model on one year worth of data,
and
forecast for the last 3 days of training data, I get a mean absolute
error
of 3.6642%. The error increases to 27.826% when I use two years worth of
data, and decreases to 17.123% when using three years worth of past
data. Is
this expected?
Also, when I plot the actual and fitted data against time, the GARCH
model
seems to have done a really bad job, compared to a default ARIMA model.
I'm
guessing this might be because people are actually using ARIMA models
with
(added) GARCH errors, so a simple GARCH model-based forecast isn't doing
exactly what they have done. Am I right? Why would the ARIMA be a better
fit
than GARCH?
One author mentioned that they took a log of the prices (in their case
it
was hourly prices) before fitting a GARCH model. In your opinion, is
that an
important factor in the kind of errors I am getting?
Thanks and best regards
--
Muhammad Saqib Ilyas
PhD Student, Computer Science and Engineering
Lahore University of Management Sciences
7:08 a.m.
Sure
I tried to import the excel data, with the first column containing trading
date, followed by weighted average price, and volume traded (in MW).
Apparently, gretl didn't quite like the date, and did not recognize the
data as a time series. I had gretl auto generate a time series
interpretation by generating a variable "cont" which has the same values as
the trading date in the excel file.
The same data has been used for price prediction in a paper in IEEE
Transactions on Power Systems of May 2005, using GARCH.
Awaiting your comments.
Thanks and best regards
On Thu, Oct 1, 2009 at 6:07 PM, Mike Pfeiff <MikeP(a)kfoc.net> wrote:
To better understand your model, could you give us an idea of the
independent variable you are using to predict NEPOOL prices? Typical
independent variables include day of week/holiday dummy variables,
natural gas price, heating oil price, weather variables, etc..
Thanks
- Mike
-----Original Message-----
From: gretl-users-bounces(a)lists.wfu.edu
[mailto:gretl-users-bounces@lists.wfu.edu] On Behalf Of
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Today's Topics:
1. GARCH for day-ahead electricity prices (Saqib Ilyas)
----------------------------------------------------------------------
Message: 1
Date: Wed, 30 Sep 2009 19:19:58 +0500
From: Saqib Ilyas <msaqib(a)gmail.com>
Subject: [Gretl-users] GARCH for day-ahead electricity prices
To: gretl-users(a)lists.wfu.edu
Message-ID:
<262b67200909300719k16ab8e46m5e63bd83bce25ddf(a)mail.gmail.com>
Content-Type: text/plain; charset="iso-8859-1"
Hi all
I am using the 2006 till 2009 data for the New England pool of day-ahead
weighted average prices. I am not very fluent with econometrics, but I
read
in some papers that GARCH has been used successfully to forecast
electricity
wholesale prices. When I train a GARCH model on one year worth of data,
and
forecast for the last 3 days of training data, I get a mean absolute
error
of 3.6642%. The error increases to 27.826% when I use two years worth of
data, and decreases to 17.123% when using three years worth of past
data. Is
this expected?
Also, when I plot the actual and fitted data against time, the GARCH
model
seems to have done a really bad job, compared to a default ARIMA model.
I'm
guessing this might be because people are actually using ARIMA models
with
(added) GARCH errors, so a simple GARCH model-based forecast isn't doing
exactly what they have done. Am I right? Why would the ARIMA be a better
fit
than GARCH?
One author mentioned that they took a log of the prices (in their case
it
was hourly prices) before fitting a GARCH model. In your opinion, is
that an
important factor in the kind of errors I am getting?
Thanks and best regards
--
Muhammad Saqib Ilyas
PhD Student, Computer Science and Engineering
Lahore University of Management Sciences
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--
Muhammad Saqib Ilyas
PhD Student, Computer Science and Engineering
Lahore University of Management Sciences
7:59 a.m.
In fact I am interrested in short term (weak ahead) electricity load
forecasting. Data is available for every 15 minutes, and the forecast
must be made on that time intervall too.
If anyone is interrested or can give tips, please contact me (I have
made some SARIMA models with other independent variables, but I might be
far from a "perfect" solution.
Have a nice day
Daniel
Saqib Ilyas írta:
> Sure
> I tried to import the excel data, with the first column containing
> trading date, followed by weighted average price, and volume traded
> (in MW). Apparently, gretl didn't quite like the date, and did not
> recognize the data as a time series. I had gretl auto generate a time
> series interpretation by generating a variable "cont" which has the
> same values as the trading date in the excel file.
> The same data has been used for price prediction in a paper in IEEE
> Transactions on Power Systems of May 2005, using GARCH.
> Awaiting your comments.
> Thanks and best regards
>
> On Thu, Oct 1, 2009 at 6:07 PM, Mike Pfeiff <MikeP(a)kfoc.net
> <mailto:MikeP@kfoc.net>> wrote:
>
>
> To better understand your model, could you give us an idea of the
> independent variable you are using to predict NEPOOL prices? Typical
> independent variables include day of week/holiday dummy variables,
> natural gas price, heating oil price, weather variables, etc..
>
> Thanks
>
>
> - Mike
>
> -----Original Message-----
> From: gretl-users-bounces(a)lists.wfu.edu
> <mailto:gretl-users-bounces@lists.wfu.edu>
> [mailto:gretl-users-bounces@lists.wfu.edu
> <mailto:gretl-users-bounces@lists.wfu.edu>] On Behalf Of
> gretl-users-request(a)lists.wfu.edu
> <mailto:gretl-users-request@lists.wfu.edu>
> Sent: Wednesday, September 30, 2009 11:00 AM
> To: gretl-users(a)lists.wfu.edu <mailto:gretl-users@lists.wfu.edu>
> Subject: Gretl-users Digest, Vol 32, Issue 36
>
> Send Gretl-users mailing list submissions to
> gretl-users(a)lists.wfu.edu <mailto:gretl-users@lists.wfu.edu>
>
> To subscribe or unsubscribe via the World Wide Web, visit
> http://lists.wfu.edu/mailman/listinfo/gretl-users
> or, via email, send a message with subject or body 'help' to
> gretl-users-request(a)lists.wfu.edu
> <mailto:gretl-users-request@lists.wfu.edu>
>
> You can reach the person managing the list at
> gretl-users-owner(a)lists.wfu.edu
> <mailto:gretl-users-owner@lists.wfu.edu>
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of Gretl-users digest..."
>
>
> Today's Topics:
>
> 1. GARCH for day-ahead electricity prices (Saqib Ilyas)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 30 Sep 2009 19:19:58 +0500
> From: Saqib Ilyas <msaqib(a)gmail.com <mailto:msaqib@gmail.com>>
> Subject: [Gretl-users] GARCH for day-ahead electricity prices
> To: gretl-users(a)lists.wfu.edu <mailto:gretl-users@lists.wfu.edu>
> Message-ID:
>
> <262b67200909300719k16ab8e46m5e63bd83bce25ddf(a)mail.gmail.com
> <mailto:262b67200909300719k16ab8e46m5e63bd83bce25ddf@mail.gmail.com>>
> Content-Type: text/plain; charset="iso-8859-1"
>
> Hi all
> I am using the 2006 till 2009 data for the New England pool of
> day-ahead
> weighted average prices. I am not very fluent with econometrics, but I
> read
> in some papers that GARCH has been used successfully to forecast
> electricity
> wholesale prices. When I train a GARCH model on one year worth of
> data,
> and
> forecast for the last 3 days of training data, I get a mean absolute
> error
> of 3.6642%. The error increases to 27.826% when I use two years
> worth of
> data, and decreases to 17.123% when using three years worth of past
> data. Is
> this expected?
>
> Also, when I plot the actual and fitted data against time, the GARCH
> model
> seems to have done a really bad job, compared to a default ARIMA
> model.
> I'm
> guessing this might be because people are actually using ARIMA models
> with
> (added) GARCH errors, so a simple GARCH model-based forecast isn't
> doing
> exactly what they have done. Am I right? Why would the ARIMA be a
> better
> fit
> than GARCH?
>
> One author mentioned that they took a log of the prices (in their case
> it
> was hourly prices) before fitting a GARCH model. In your opinion, is
> that an
> important factor in the kind of errors I am getting?
>
> Thanks and best regards
>
> --
> Muhammad Saqib Ilyas
> PhD Student, Computer Science and Engineering
> Lahore University of Management Sciences
>
5338
days inactive
5339
days old
2 comments
3 participants
participants (3)
-
Kehl Dániel -
Mike Pfeiff -
Saqib Ilyas