Timeline for Why is my kalman filter trusting so much my observations?
Current License: CC BY-SA 3.0
6 events
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
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| May 25, 2016 at 16:23 | vote | accept | Ben | ||
| May 25, 2016 at 15:07 | comment | added | Cagdas Ozgenc | I cannot debug your code. However I can explain you what is happening as I tried to fit various Kalman models to stock prices in the past. Stock prices follow a random walk model. There is almost 0 observation noise, unless it is intraday tick data, in which case observation noise is the bid-ask oscillation (which is irrelevant for prediction purposes). Basically all your noise comes from the process itself which makes it very unpredictable based on historical price information. Basically when you observe a price it is actually the underlying state, hence there is nothing to filter. | |
| May 25, 2016 at 13:53 | answer | added | ylnor | timeline score: 2 | |
| May 25, 2016 at 13:12 | history | edited | Ben | CC BY-SA 3.0 |
edited title
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| May 25, 2016 at 13:06 | history | asked | Ben | CC BY-SA 3.0 |