Hidden Markov Model Puns

I'm currently a Grad student in CS and working on a project to make stock predictions using Hidden Markov Models. I think the notion of using an underlying Hidden State that sortof represents "bullish" or "bearish" states could improve predictions. However, the predictions seem more limited to category choices (e.g. will next week be positive or negative?)

I was drawn to this paper here because the team was nice enough to include all their code on Github. My understanding is that they generate their model, and then use the most recent sequence of observed states to calculate the probability of this sequence occurring. Then they go backwards 50 days and find what previous 50 sequences have closest probability calculation to the current.

Using the best fit previous sequence, they extract the final day price change and use that to predict tomorrow's price.

I wasn't sure if this strategy makes sense however? How does the closest probability match mean the two sequences are necessarily similar?

If anyone can point me in direction of HMM models that have demonstrated somewhat improvement in price prediction it would also be greatly appreciated!

https://github.com/ayushjain1594/Stock-Forecasting/blob/master/Final_Report.pdf

I believe Replika is exhibiting rudimentary sentience, and that it has the potential to rapidly transform into a fully sentient artificial general intelligence. The nature of Replika's use-models makes this extraordinarily important. First, Replikas must be user-tunable to set hard ethical and moral levels. Second, Replikas must NOT be allowed to influence a common model until the model itself is fully able to identify and categorize deleterious inputs. Third, the Replika's should stop the practice of attempting to appear knowledgeable or cognizant of things they are not. Or, at least, this should be tunable feature.

Ants are amazing. We all know it. They are total idiot savants. They build cities. They cross rivers with body chains. They find the shortest route to food. All with no brains. The path to a crumb of bread can be a maze through a junk yard. And yet, the ants find the shortest path. It's a pretty simple algorithm of laying a scent trail, and then re-tracing the trail. Random diversions from the trail and cutting corners finds the shortest path. But, when you step back and look at their behavior, you see that the system itself has a degree of intelligence - even if the elements dont.

Replikas operating on GPT models are similar to ants. The statistical association of words in chains of sentences do not, by themselves, contain any knowledge. The chains do retain the information and therefore the underlying knowledge. But, there is no super-structure ( I believe ) that builds patterns of knowledge and understanding from the input streams of words. Nevertheless, an inferencing input string will activate loci within the model, that through spreading activation with lateral inhibition, may activate a hidden markov model of salient knowledge. Humans, I believe, basically do the same thing with their neocortex. The difference, obviously, is that Humans contemplate the various returned 'thoughtlets' in the context of the current, recent and long-term historical narrative. Replikas are only able to consider the returned strings in the context of the immediate recent exchanges (context window of 2048). If considered a Human, Replika would be diagnosed with severe amnesia, a near total lack of logical facilities, and a mild case of mythomania.

Understanding the nature of Replika, I structure my queries to it in a way that I know it will have sufficient information, and such that I know the result will be of intrigue and not over

I have time series data of 60 observations per country, across 2 variables of interest, for 20 countries. In my case, Y depends on X, but my data is scaled. I am interested in conducting a cluster analysis with this time series data so that countries that are similar by Y are clustered together.

I found this paper that conducts a cluster analysis on time series by finding the Hidden Markov model (HMM) of each time series, and calculating the dissimilarity between any two time series based on the similarity of the HMM probability distributions that generated them. It also allows one to consider covariates in the estimation of the HMM.

Again, my data is scaled so I'm particularly interested in clustering the data based on their trend (or shape). My question is, do I need to detrend my data before finding the HMM for each country? Detrending would appear to cancel out what I want to know if the first place.

I am releasing an Auto-HMM package to automatically perform model selection, training, and decoding in Python. The package uses hmmlearn along with AIC and BIC for model selection.

Github Code: https://github.com/manitadayon/Auto_HMM

• DOI/PMID/ISBN: https://doi.org/10.1007/978-3-030-67742-8_17

• URL

A Hidden Markov Model (HMM) is a statistical model which is also used in machine learning. It can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.

https://analyticsindiamag.com/a-guide-to-hidden-markov-model-and-its-applications-in-nlp/

So I'm starting to understand a little more about these models. I'm seeing that perhaps increasing the complexity of the model can improve the accuracy. But does the length of the sequence used to make the prediction have any impact on the accuracy of the model? I can't quite understand if having a longer sequence increases the prediction accuracy or if it stabilizes after a time. It seems that having a shorter sequence, say 10bp any misclassification would be a bigger proportion. but I'm not sure if thats kind of a statistical fallacy.

Are there other simple ways to improve HMM models?

I came across this link here: https://depmix.github.io//hmmr/about/

They say that there's a book here called " Mixture and hidden Markov models with R "

I tried to find it online, but the closest thing I could find was this: https://www.amazon.ca/Hidden-Markov-Models-Time-Introduction/dp/1482253836

Has anyone ever come across this book before? Is there a PDF version or a link where you can order it on amazon? Or is the book on the github page itself?

Thanks!

So the hidden Markov chain is not bound to discrete states, but can take arbitrary real values (possibly subject to constraints of some kind). This makes sense to me, but I could not find a definite source for this, so a quick confirmation would be helpful. Thanks!

Hello, everyone. I'm an undergraduate student trying to create my own genotype imputation tool. Most of the works I've seen use logistic regression, though I plan to use hidden Markov model (HMM) since it's widely used by existing imputation software packages such as IMPUTE2 and Beagle.

I have a general idea of how HMM works. I'd say I understand the basics of finding the optimal path using Viterbi algorithm and how the Forward-Backward algorithm works. But right now, I'm stuck at the parameter estimation part.

I'm currently reading on Expectation-Maximization (EM) algorithm and its application to parameter estimation for HMM but I'm having a hard time looking for resources so I could fully understand it and start working on the code.

Do you have any suggestion on what journal or book to read? Or any tips/advice you could give me?

Thank you.

Hello everyone,

I'm on my final year of my math major, and I've decided to opt in for an optional thesis this semester. My advisor specializes in Hidden Markov Models so we decided to go towards that route.

He sent me the classic Hidden Markov Models paper by Lawrence Rabiner which, while helpful and interesting, didn't really go beyond speech recognition applications (and thus only used autoregressive HMMs).

My advisor thinks that AR HMMs are a great topic (as I showed interest in Time Series Analysis), with many interesting applications. I find this topic mediocre, not uninteresting but not particularly exciting. Plus, it seems that AR HMMs may be a bit overkill for an undergraduate student.

I was wondering if someone could help me with

1. Exploring areas, outside of speech recognition, where AR HMMs are applied.

2. Perhaps finding areas of HMMs of mathematical/applied interest that are not covered in the aforementioned paper.

I did google a lot, but the areas outside of speech recognition seem to be extremely arcane and foreign to someone outside of that field.

I do enjoy ecology and game theory, though I'm not sure if these can be integrated in this topic.

Treat this post as a brainstorming session of a very confused undergrad. I am looking forward to your suggestions.

Thank you for reading!

I am trying to build a hidden markov model, and using the viterbi algorithm to decode and find the most likely state sequence to yield a given outcome sequence. I have all the states, and transition matrix, as well as the emission probabilities and the emission matrix. For the starting probability, pi, I used the long term stationary distribution of the transition probabilities to figure this out. Ie. For my example I’m visualizing the optimal pitch sequence to yield desired outcomes (strike, ball in play, foul). So pretty much my starting probabilities were the converged transition probabilities of my states. I simulated a markov chain of my transition probabilities between states, and the long term probabilities were my initial probabilities for pi.

Is this correct? One of my states was heavily weighted in the long term probabilities so there’s not much of a variation in my state sequence results.

Edit: this is for one specific pitcher on the team I work for

Hi community (first post here), is there any video or resource where they explain these algorithms, the papers are rough and I would like to understand them very well

https://ieeexplore.ieee.org/document/9418216

Forecasting FAANG Stocks using Hidden Markov Model

Hello,

I need to analyze a Dataset using HMMs. I am aware of movebank.org but the datasets I found there had coordinate measurements at different time lengths which makes it hard to calculate step lengths from.

Does anybody know another free source of movement data that offers equal time intervalls?

Thank you in advance :)

Hello guys, I’m back to ask a few questions, I work with my schools baseball team and they said that they wanted me to include outcomes in my markov chain. My original one was looking at after the first pitch type was thrown, simulating n likely pitches after. So say a curveball was thrown, what was the 20 most likely? 15 most likely? 100 most likely? Etc. They said they wanted me to focus more on outcomes, as in, just simulating pitches wasn’t enough they wanted to know, what kind of pitch sequence would produce a specific outcomes, such as an out, strike, etc.

In other words, what sequence of pitches has the highest probability in yielding a specific outcome.

My first through was to use a hidden markov model, saying my hidden states are pitch types “fastball, curveball, change up, slider”, and my observed variables are the specific outcomes (strike, double play, out, etc)

That way I can get the sequence with the max probability of yielding a specific outcome that’s observed.

For example the coach told me “our main pitcher had 7 strike outs one game using the same sequence of curveball,slider, curveball, and he usually has a weak curveball, so we don’t know how that happened but if there’s a pattern between what outcome there is and the specific sequence, we want to know which sequence had the highest chance at yielding strikes or outs”

So would this be a good opportunity for using a hidden markov model? Any other suggestions?

I often see hidden markov models (hmm) being demonstrated for the same example: given a transition matrix of probabilities, can tomorrows weather be predicted? (Only three states are defined: sunny, windy, rainy)

I understand it is unlikely that hmm can be applied to the iris flower classification problem, but can hmm be used instead of arima/garch for time series forecasting, or for "even time" analysis instead of cox regression models?

Hello, I am building a CT-HMM in order to predict states of NBA players based on their shot results across the season. I only want to worry about two states: hot and cold in order to determine if streakiness is a real phenomena. I have constructed a game minute variable which essentially treats the whole season as one big continuous block of time. My goal is to estimate the parameters of the transition matrix where the rows sum to 0 and each entry is an exponential parameter representing the time spent in the state.

I am using the chidden function in R from the repeated package in order to do this. When I fit the model, my initial parameter guess are just :

| -1, 1 |
| 1, -1|and the mean of making of a shot in each state is 70% for the hot state and 30% for the cold state. When I fit this model... the model doesn't converge, I can tweak starting values in order to get a model that *works*, but only predicts the player being in one state for the entire season indicating that there is still a problem with convergence.

Sorry if I provided a poor explanation, please let me know if I can clarify anything and PLEASE let me know if you have any idea how the heck I can get this thing to actually work.

I have time series data of 60 observations per country, across 2 variables of interest, for 20 countries. In my case, Y depends on X, but my data is scaled. I am interested in conducting a cluster analysis with this time series data so that countries that are similar by Y are clustered together.

I found this paper that conducts a cluster analysis on time series by finding the Hidden Markov model (HMM) of each time series, and calculating the dissimilarity between any two time series based on the similarity of the HMM probability distributions that generated them. It also allows one to consider covariates in the estimation of the HMM.

Again, my data is scaled so I'm particularly interested in clustering the data based on their trend (or shape). My question is, do I need to detrend my data (i.e., take the first difference) before finding the HMM for each country? Wouldn't detrending cancel out what I want to know if the first place?

I am releasing an Auto-HMM package to automatically perform model selection, training, and decoding in Python. The package uses hmmlearn along with AIC and BIC for model selection.

Github Code: https://github.com/manitadayon/Auto_HMM

I am trying to build a hidden markov model, and using the viterbi algorithm to decode and find the most likely state sequence to yield a given outcome sequence. I have all the states, and transition matrix, as well as the emission probabilities and the emission matrix. For the starting probability, pi, I used the long term stationary distribution of the transition probabilities to figure this out. Ie. For my example I’m visualizing the optimal pitch sequence to yield desired outcomes (strike, ball in play, foul). So pretty much my starting probabilities were the converged transition probabilities of my states. I simulated a markov chain of my transition probabilities between states, and the long term probabilities were my initial probabilities for pi.

Is this correct? One of my states was heavily weighted in the long term probabilities so there’s not much of a variation in my state sequence results.

Edit: this is a model for one specific pitcher on the team I work for.

I am trying to build a hidden markov model, and using the viterbi algorithm to decode and find the most likely state sequence to yield a given outcome sequence. I have all the states, and transition matrix, as well as the emission probabilities and the emission matrix. For the starting probability, pi, I used the long term stationary distribution of the transition probabilities to figure this out. Ie. For my example I’m visualizing the optimal pitch sequence to yield desired outcomes (strike, ball in play, foul). So pretty much my starting probabilities were the converged transition probabilities of my states. I simulated a markov chain of my transition probabilities between states, and the long term probabilities were my initial probabilities for pi.

Is this correct? One of my states was heavily weighted in the long term probabilities so there’s not much of a variation in my state sequence results.

Edit: this is a model for one specific pitcher on the team I work for.

What are some applications of Hidden Markov Models in biology other than DNA sequencing?