- You are only using 10 particles, which is a fairly small number. Consider increasing it. The particle filter is really good at approximating non-Gaussian probability distributions, but it requires a good number of particles to do that. Otherwise, you might see particle starvation.
- For prediction, the particle filter uses the StateTransitionFcn function handle specified in the stateEstimatorPF object. By default, this will just spread the particles based on a zero-mean normal distribution. You probably want to set the StateTransitionFcn to some function that is more useful for your data. For example, if your data is captured from a vehicle moving at constant velocity, you could use a constant velocity state transition (constvel). There is no one-size-fits-all approach.
Time series forecasting with Particle Filter, How can I Do?
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Geraldo Cesario Junior
am 24 Jan. 2022
Kommentiert: Geraldo Cesario Junior
am 1 Feb. 2022
I´trying to implement a Time Series Forecasting Using Particle Filter.
Attached are the files that i am working on.
1) Particle Filter Prediction.csv, is the file with two coluns that contem the information.
Column Index, is a sequencial number of occurrencies ( 1 to 2419 )
and column Result that is the value of each Index occurrency.
2) Projeto_ParticleFilter_forecasting is the Mathlab code File.
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Remo Pillat
am 27 Jan. 2022
Hi Geraldo,
I'm not entirely sure about the source of your data or your end goal, but I noticed a few things in your code:
Hope this helps.
Thanks,
Remo
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