1. A method for identifying an anomaly in a signal, comprising: receiving a discrete signal, having sample values corresponding to amounts of data flow in a network within a time interval; generating a sequence of likelihoods corresponding to sample values in the signal and based at least in part on a historical probability distribution of previously received sample values corresponding to amounts of data flow in the network, wherein a likelihood is a probability of occurrence of a corresponding sample value in the signal; identifying likelihood change points in the likelihood sequence by: selecting a parameter L corresponding to a minimum number of samples in a segment; appending L consecutive likelihoods to a buffer; computing a sequence of first sum values of the likelihoods in the buffer; obtaining a sequence of second sum values; determining the presence of a change point in the buffer based at least in part on a comparison between the first and second sum values, wherein a plurality of likelihoods preceding the change point have a first statistic value and a plurality of likelihoods following the change point have a second statistic value different from the first statistic value; and identifying a likelihood in the buffer as a change point based at least in part on the comparison; segmenting the discrete signal into a plurality of segments at samples corresponding to the identified change points such that a respective one of the samples corresponding to the identified change points is at one of a beginning or an end of each of the plurality of segments; identifying a segment as an anomaly based on a comparison between a statistic of the segment and a statistic of the historical probability distribution; and reconfiguring, responsive to identifying the segment as the anomaly, a component of the network.