Fixed-delay smoothing in HMM with Numpy

Let’s consider a Hidden Markov Model describing a sequential problem: a system has three internal (hidden) states: Ok (Everything works correctly) Some issues (not blocking) Out of order However, we can observe only a sensor (globally connected with different sub-systems) which states are represented by three colors (green, yellow and red), representing respectively a normal, partially dangerous and absolutely risky situation. They are directly connected with a precise component, so we don’t know if there’s a failure or a false-positive (sometimes another sensor can fail). We can observe this sequence and try to predict the internal states. That’s what you can achieve with HMM. I cannot expose all the theory behind them however you can find some good references at the end of this post. This is a graphical representation of such a process (only 5 time-steps). A prediction can be achieved using an algorithm called “Fixed-delay Smoothing” which combines forward […]