# Tagged Questions

It describes the probabilistic dependence between the latent state variable and the observed measurement.

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### Difference between particle filter (PF) and recurrent neural network (RNN) for time series

Both method are used to estimate time series from data. The question is, when should I use one method or other? Is any advantage to use one instead of the other? I know that in a PF there is a hidden ...
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### Predicting a Chi-Square Process

Assume that $W(t)$ is a one-parameter stochastic process given by $W(t) := X_1^2(t) + X_2^2(t)$ where $X_i(t)$ are independent copies of a stationary gaussian process with known covariance function. ...
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### State space modelling of longitudinal data in r

I have n stations, and for each station there are m time series observations on different days, each of length ...
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### Intuitive explanation of state space models

Having looked into options for modelling and forecasting a financial time series based on mixed frequency data, I came across state space models, which seems worth exploring. I've however been ...
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### Maximum Likelihood estimation and the Kalman filter

I know the Kalman filter recursions and can derive these but what I don't really get is how to estimate the hyper parameters using maximum likelihood. I understand that when running the Kalman filter ...
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### How to represent an ARIMA(p,d,q) with dlm package in R? [closed]

I've been using DLM package for modeling my timeseries in state-space format, and then use Kalman Filter to get better 2 step-ahead forecasts. Even though I've read the vignette and parts of their ...
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### How to use a Hidden Markov Model to detect state in a time series?

Questions Am I right in assuming that the emission probabilities will not be following a gaussian distribution for my particular problem? Obviously, I will need to train the model for state ...
Lets say I have the following state space model: $y_t = \beta_t x_t + \epsilon_t$ $\beta_{t+1} = \mu_t + \beta_t \eta_t$ $\mu_{t+1} = \mu_t + \omega_t$ All my true values for $y$ are known, but I ...