A quick update of my work.

In the last meeting, we discovered that the Poisson process is not  a good model to apply in our scenario. It considers events are independent, while they are dependent in our case. Therefore, I need to switch to another appealing model, which should be done before 01 January 2012.

The strategy is to looking for models which work with dependent data. In addition, I need to gain much knowledge about machine learning and other mathematical models, so I am able to judge and select the right one.

I kick off with the paper of "Unsupervised Disaggregation of Low Frequency Power Measurements", which has been given by Oli. The paper mainly discusses the effectiveness of several unsupervised disaggregation methods using the factorial hidden Markov model on low frequency power measurements collected in real homes. In their model, the states of appliances are the hidden variables, and the aggregate power load is the observation. Therefore, they chose variants of Hidden Markov Model (HMM). More specially, they extend a Conditional Factorial Hidden Semi-Markov Model (CFHSMM), which allows the model to consider the dependencies between appliances and the dependencies on additional features, that I think is quite relevant to our case. Then, they apply machine learning process to estimate the parameters from the observations, and the hidden variables (which is the states of the appliances). Specifically, they use Expectation-Maximization algorithm (EM) to estimate the parameters, then using Maximum Likelihood Estimation (MLE) to estimate the hidden states.

In our case, the events are possibly annotated by users. So, I think Hidden Semi-Markov models could be used. I will check more references paper on CFHSMM to see if there is any relevant existing models.

Furthermore, I have grasped some book to read with the hope to have a better overal view on machine learning models. I will check out this list:

- Chapter 9: Mixture Models and EM (423-455) (book " Pattern Recognition and Machine Learning" - Christopher M. Bishop).

- Chapter 6: Bayesian Learning (154-199) (book: "Machine Learning" - Tom M. Mitchell).