Table of Contents
Why is RNN better than HMM?
If the assumptions are true then you may see better performance from an HMM since it is less finicky to get working. An RNN may perform better if you have a very large dataset, since the extra complexity can take better advantage of the information in your data.
Why RNN is good for time series data?
The good performance of the Vanilla RNN, which does not integrate the “long” aspect of the LSTM algorithm, implies that the time series follows a pattern that does not require much of a long-term memory. RMSE, which squares the prediction errors, penalizes larger errors more than MAPE does.
What is the difference between Markov and hidden Markov models?
Markov and Hidden Markov models are engineered to handle data which can be represented as ‘sequence’ of observations over time. Hidden Markov models are probabilistic frameworks where the observed data are modeled as a series of outputs generated by one of several (hidden) internal states.
Is Markov model a finite state machine?
Markov Model as a Finite State Machine from Fig.9. data —Image by Author The Viterbi algorithm is a dynamic programming algorithm similar to the forward procedure which is often used to find maximum likelihood.
The intro to the Wikipedia page on Hidden Markovich Models explains it concisely: “Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states. The hidden markov model can be represented as the simplest dynamic Bayesian network.”
What is the difference between order-k Markov and stationary process?
An order-k Markov process assumes conditional independence of state z_t from the states that are k + 1-time steps before it. 2. Stationary Process Assumption: Conditional (probability) distribution over the next state, given the current state, doesn’t change over time.