I am working on a hmm for financial time series price data using the hmmlearn package (http://hmmlearn.readthedocs.io/en/latest/). I would like to implement a 2 or three state model to fit my data on. However I would like to have different distributions depending on the hidden state. It seems you can implement a custom emission probability using the _BaseHMM override. However I am not completely sure how to do this form the documentation. Are there any available example or could somebody please provide how to set e.g. two different emission probabilities for each state in a hmm framework like this?
Many thanks in advance.
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I have search and saw some questions on the matter but without answer (due to the fact that the questions were asked more than 1 year ago, I. hoped something has changed)
I am looking for a library to infer bayesian network from a file of continious variables is there anything simple\out of the box that any one has encountered? I have tried pyAgrum for example but when i run
pyAgrum.BNLearner(numdata).learnDAG()
I get
Exception: [pyAgrum] Wrong type: Counts cannot be performed on continuous variables. Unfortunately the following variable is continuous: V0
Have tried serval libraries but they all seem to work only on discrete variables would love some help in advance.
The main question is what kind of model do you want for your continuous variables.
1- Do you want them to be discretized : you can have a look for instance at http://webia.lip6.fr/~phw/aGrUM/docs/last/notebooks/Discretizer.ipynb.html.
2- Do you want to assume a linear gaussian model : you can have a look for instance at bnlearn (https://haipengu.github.io/Rmd/GBN.html)
3- Do you want to learn more general continuous model : You can have a look at for instance otagrum (http://openturns.github.io/otagrum/master/) which learns copula bayesian network.
4- etc.
I want to predict company's sales. I tried with LSTM but all the examples that I found only use two variables (time and sales).
https://www.kaggle.com/freespirit08/time-series-for-beginners-with-arima
This page mentioned that time series only use two variables but I think that is not suficient to build a good forecast. After this, I found different 'multiple features' options like polynomial regression with PolynomialFeatures from sklearn or regression trees. I haven't write a script with these last algorithms yet, then I wanna know your recommendations about what model to use.
Thanks.
You could try Facebook's Prophet, which allows you to take into account additional regressors, or Amazon's DeepAR.
But I also have seen forecasting models based not on ARIMA style time series but on simple linear regression with extensive feature engineering (features=store+product+historical values) in production.
Hope this helps.
I would recommend using Prophet. As this has certain advantages over conventional models like ARIMA:
It take cares of empty value well.
Tunning its parameters is way easier and intuition based.
Traditional time series forecasting model expects data points to be in consistent time interval. However, that’s not the case with “Prophet”. Time interval need not to be same throughout.
For a project I am working on, I need to find a model for the data graphed below that includes a sine or cosine component (hard to tell from the image but the data does follow a trig-like function for each period, although the amplitude/max/mins are changing).
data
I originally planned on finding a simple regression model for my data using Desmos before I saw how complex the data was, but alas, I do not think I am capable of determining what equation to use without the help of Python. I don't have much experience with regression in Python, I've only done basic linear modeling where I knew the type of equation and was just determining the coefficients/constants. Could anyone offer a guiding example, git code, or resources that would be useful for this?
Your question is pretty generic and looking at the graph, we cannot tell much about the data to give you a more detailed answer, but i'd say have a look at OLS
https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html
You could also look at scikit learn for the various regression models it provides.
http://scikit-learn.org/stable/modules/linear_model.html
Essentially,these packages will help you figure our the equation you are looking to have for your data.
Also, looks like your graph has an outlier ? Please note regression is very sensitive to outliers, so you may want to handle those data points before fitting the model.
Is there a way to have an x,y pair dataset given to a function that will return a list of curve fit models and the coeff. The program DataFit does this with about 200 different models, but we are looking for a pythonic way. From exponential to inverse polynomial etc.
I have seen many posts of manually using scipy to type each model, but this is not feasible for the number of models we want to test.
The closest I found was pyeq2, but this is not returning the list of functions, and seems to be a rabbit hole to code for.
If R has this available, we could use that but python is really the goal
Below is an example of the data, we want to find the best way to describe this curve
You can try library splines in R. I have used this for higher order curve fitting to some univariate data. You can try to change and achieve similar thing with corresponding R^2 errors.
You can either decide to do the following:
Choose a model to fit a parameters. This model should be based on a single independent variable. This can be done by python's scipy.optimize curve_fit function. You can choose something like a hyberbola.
Choose a model that is complex and likely represents an underlying mechanism of something at work. Like the system of ODE's from a disease SIR model. Fitting the parameters will be no easy task. This will be done by Markov Chain Monte Carlo (MCMC) methods. This is VERY difficult.
Realise that you have data and can use machine learning via scikit learn to predict from your data. This is a method that doesn't require parameters.
Machine learning and neural networks don't fit something and can't really tell you about the underlying mechanism but can make predicitions just as a best fit model would...dare I say even better.
In the end, we found that Eureqa software was able to achieve this. https://www.nutonian.com/products/eureqa/
I am trying to train a Hidden Markov Model (HMM) using the GHMM library. So far, I have been able to train both a discrete model, and a continuous model using a single Gaussian for each of the states.
There are really good examples on how to do it here.
However, I would like to train a continuous HMM with a single covariance matrix tied across all states (instead of having one for each state). Is that possible with GHMM lib? If it is, I would love to see some examples. If not, could somebody point me to some other code, or refer me to another HMM python/c library that can actually do it?
Thank you!
So, I have found this great package in C that has an HMM implementation exactly the way I wanted: Queen Mary Digital Signal Processing Library. More specifically, the HMM implementation is in these files. So, no need to use GHMM lib anymore.