Timeline for Creating synthetic data for time series, Hidden Markov Model
Current License: CC BY-SA 4.0
4 events
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| Sep 26, 2022 at 17:01 | comment | added | phil | You can also use it to ask questions about classification accuracy, by looking at the hidden states that you infer and comparing them to the true states (that you know because the data are simulated.) The question this answers is roughly: Assuming that my parameter estimates are correct, how good will this classifier perform on other data generated by this process? | |
| Sep 26, 2022 at 16:56 | comment | added | phil | If you do the process that I described, you can then estimate the parameters from your new simulated dataset. If you need a lot of transitions, I would suggest simulating ~1000 sequences and doing the estimation on each one. Some will have more transitions and some will have less, and the parameters you estimate will vary on each simulation. But since you know the true parameters that generated them, you can use that knowledge to accuracy and precision of the estimator. This is called Markov-chain Monte Carlo | |
| Sep 25, 2022 at 12:08 | comment | added | thesecond | Thanks for the response. The initial goal is to assess the performance of my classifier. In order to do that, I simply need more transitions between the states and it therefore more data, so I cannot really generate it from my model. | |
| Sep 25, 2022 at 7:16 | history | answered | phil | CC BY-SA 4.0 |