If you aren't a trend follower within the quant price space universe, then you are a mean reverter. A paper discusses how to filter these reversion signals, "Advanced signal filtering for mean reversion trading." Mean reversion is based on the simple concept that an asste's price will converge to some fair value. This fair value could be as simple as a moving average. The spot price may fall above or below this fair value price. To solve this problem, the authors develop what they call the local average filtering objective (LAFO), a low-pass filter that operates across different frequencies. LAFO examines the average residuals over a moving window to capture moving-average characteristics. This information can be used to measure or describe mean reversion. LAFO is an extension of the mean squared error. Machine learning can help process data to identify the method of reversion to the mean and mispricing in a time series.
Can there still be dislocations that will cause mean-reversion not to occur? Yes, but by examining different rolling windows of residuals, there is a good chance of finding revision opportunities.
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