What makes a hidden Markov model different than linear regression or classification?
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| Papers (9) | Insight |
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| It extends previous work on homogeneous Markov chains to more general and applicable hidden Markov models. | |
14 Citations | We also show that hidden Markov models can be used according to the right choice of parameters. |
Related Questions
How do coupled Hidden Markov Models improve the accuracy of sequence classification tasks?5 answersCoupled Hidden Markov Models (HMMs) enhance sequence classification accuracy by integrating various data sources and improving modeling capabilities. These models address the limitations of traditional HMMs by incorporating additional information, such as stock quantification and news event data, to mitigate sparse data issues. Furthermore, advancements in training methods, like utilizing partial labeling data, have shown significant improvements in model accuracy for decoding synthetic and real biological sequence data. Additionally, the incorporation of deep neural networks and continuous latent processes in triplet Markov chains provides a more robust framework for unsupervised classification tasks, outperforming classical HMMs and their extensions. Overall, these innovations in coupled HMMs offer a more comprehensive and accurate approach to sequence classification tasks.
Does linear regression without data training have limited prediction capabilities compared to data-trained models?5 answersLinear regression without data training does have limited prediction capabilities compared to data-trained models. Nonlinear regression (NLR) models, which are often used in environmental sciences, can perform slightly better or worse than linear regression (LR) models. However, NLR models can give predictions much worse than LR when given input data that lie outside the domain used in model training. This is because NLR models struggle with extrapolation to new input data that is far outside the training domain. To address this issue, an approach called NLROR (nonlinear regression with Occam's Razor) has been proposed, where linear extrapolation is used for outliers based on the NLR model within the non-outlier domain. NLROR tends to outperform both NLR and LR for outliers, improving the reliability of predictions in these cases.
How effective are Hidden Markov Models in predicting market trends for algorithmic trading?4 answersHidden Markov Models (HMMs) have shown effectiveness in predicting market trends for algorithmic trading. HMMs have been applied in various fields, including quantitative investment in the financial market. Studies have used HMMs to forecast stock prices and have achieved accurate predictions for different stock features such as open, close, high, and low prices. HMMs have also been used to understand finance variables in the stock market, exploring relationships between changing share values and influencing indicators. Additionally, HMMs have been utilized to predict stock exchange indices, resulting in improved accuracy and less prediction errors compared to other Markov family models. HMMs combined with other models like ARIMA have been used to find financial market trends, aiding decision-making in stock trading. Overall, HMMs have demonstrated their effectiveness in predicting market trends for algorithmic trading, providing valuable insights for investment strategies.
Why is feed-forward hidden markov models better than correlational analysis ?5 answersFeed-forward hidden Markov models (FFHMMs) are considered better than correlational analysis because they provide observation to observation linkages, which is a limitation in traditional HMMs. FFHMMs have been shown to increase the classification rate for sparse messy data and offer a new theory towards changing the way HMMs are conceived. In contrast, correlational analysis does not provide this linkage and may not capture the temporal dependencies between observations. FFHMMs have been successfully applied in various fields such as visual understanding and animal biotelemetry, where sequential data with natural dependence between observations is common. They have been used to predict states and make inferences about drivers of behavior, allowing for a deeper understanding of animal activity and behavior. Therefore, FFHMMs offer a more comprehensive and accurate approach compared to correlational analysis in capturing temporal dependencies and making predictions based on sequential data.
How can hidden Markov models be used for portfolio optimization? Code using python?5 answersHidden Markov models (HMMs) can be used for portfolio optimization by incorporating regime-switching behavior and capturing the time-varying nature of financial markets. HMMs allow for modeling the assets of a portfolio through a hidden state process, where the drift and volatility can switch between different states. This enables the portfolio to react to changes in market conditions and avoid left tail events. By utilizing HMMs in portfolio optimization, researchers have found that their strategies often outperform naive investment strategies, such as equal weights. Additionally, model predictive control can be used to dynamically optimize the portfolio based on forecasts from the HMM. Python provides an open-source implementation of HMMs, called PyHHMM, which includes features like different initialization algorithms, missing data inference, and model order selection criteria.
What are some applications of the Hidden Markov Model in computer vision?3 answersHidden Markov Models (HMMs) have various applications in computer vision. One application is in the reconstruction of neuronal processes in brain imaging, where HMMs can automatically trace neuronal processes from sub-micron resolution images. HMMs can also be used in visual inspection tasks, such as analyzing fixations' sequences during quality control inspections. In this application, eye tracking data is gathered, and HMMs are used to analyze the differences between expert and novice operators. Another application is in proximity capacitive sensors for user gesture recognition. HMMs can be used to build models that recognize and classify user gestures in real-time, providing satisfactory response and accuracy. Overall, HMMs offer valuable tools for solving problems related to incomplete observations, noise in measurements, and modeling non-Gaussian data in computer vision applications.