Weird Random Walks: Synthetizing, Testing and Leveraging Quasi-randomness
I discuss different types of synthetized random walks that are almost perfectly random, in one and two dimensions. Besides the theoretical interest, it provides new modeling tools, especially for physicists, engineers, natural sciences, security, fintech and quant professionals.
The kind of irregularities injected in these random walks are especially weak and hard to detect. The research results presented here are new, focused on applications, and state-of-the-art. In addition to offering original modeling tools, these unusual stochastic processes can be used to benchmark fraud detection systems or to benchmark tests of randomness.
The picture below features a metric that magnifies the very weak patterns, to show that despite all appearances, something is “off”, and definitely not random in my simulated random walks. You can fine-tune various parameters in the accompanying Python code, to produce different types of non-randomness, ranging from totally undetectable to hard to detect.
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