mfe_regime.m

Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach" by Rafa&#322 Weron, p

%MFE_REGIME Illustrates Case Study 4.4.5.
%
%   Type "mfe_regime" at the command line to run a script which illustrates 
%   Case Study 4.4.5 from [1].
%
%   Reference(s):
%   [1] R.Weron (2007) 'Modeling and Forecasting Electricity Loads and 
%   Prices: A Statistical Approach', Wiley, Chichester.   

%   Written by Jakub Jurdziak and Rafal Weron (2006.09.21)
%   Copyright (c) 2006 by Rafal Weron


% Load Nord Pool data
Data = load('NP_daily_des.dat');
Data = Data(:,2);

% Calibrate MRS model with Gaussian spikes  
[Ksi_tT, Param, P, Ksi_t1t_10, LogL] = mrs_est(Data, 'G');
% Plot results 
h = figure(1);
set(h,'name','2-state MRS model with Gaussian spikes','numbertitle','off');
mrs_plot(Ksi_tT(:,2), Data); 

% Calibrate MRS model with lognormal spikes  
[Ksi_tT, Param, P, Ksi_t1t_10, LogL] = mrs_est(Data, 'LN');
% Plot results 
h = figure(2);
set(h,'name','2-state MRS model with lognormal spikes','numbertitle','off');
mrs_plot(Ksi_tT(:,2), Data); 

% Calibrate MRS model with Pareto spikes  
[Ksi_tT, Param, P, Ksi_t1t_10, LogL] = mrs_est(Data, 'P');
% Plot results 
h = figure(3);
set(h,'name','2-state MRS model with Pareto spikes','numbertitle','off');
mrs_plot(Ksi_tT(:,2), Data); 

% Calibrate MRS model with a mean-reverting process for spikes  
[Ksi_tT, Param, P, Ksi_t1t_10, LogL] = mrs_est(Data, 'MR');
% Plot results 
h = figure(4);
set(h,'name','2-state MRS model with a mean-reverting process for spikes','numbertitle','off');
mrs_plot(Ksi_tT(:,2), Data);