Timeline for Time series probability and mutual information
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Jul 12, 2012 at 15:30 | comment | added | Xodarap | @user: Entropy can be considered "uncertainty", so a reduction in entropy means you are more certain of the outcome after symbolization. (Which is almost guaranteed to happen, since you're combining symbols.) For questions about a specific library, Stack Overflow is probably a better site than this, but my guess is the lib is using a different base for the logarithm. | |
| Jul 12, 2012 at 4:04 | comment | added | user1214586 | What is meant by vec1,vec2?Also,the result of entropy going by your formula, I normalized the entropy to, entropy = entropy / log2(100) is approx 0.539 whereas if I use the program provided in the link gives result as 0.21!! What is the implication of this reduced value and why is there a difference? | |
| Jul 12, 2012 at 3:45 | vote | accept | user1214586 | ||
| Jul 11, 2012 at 18:53 | comment | added | user1214586 | Thank you for this reply.However,I am unable to obtain the discretization for non binary case since the statement s=x(:,1) > 0.5; is a binary and would give 0,1 only.I wanted to know how is it possible to partion and assign more symbols and hence calculate the probability.The idea is along the lines of data mining where a data value is assigned a symbol and all such similar data values are assignedthat particular symbol. If the value of entropy for original time series is 0.90 and after symbolization it comes down to 0.52 then waht does this indicate? | |
| Jul 11, 2012 at 14:09 | history | answered | Xodarap | CC BY-SA 3.0 |