Electricity is a vital part of our daily life; therefore it is
important to avoid irregularities such as the California Electricity
Crisis of 2000 and 2001. In this work, we seek to predict anomalies
using advanced machine learning algorithms. These algorithms are
effective, but computationally expensive, especially if we plan to
apply them on hourly electricity market data covering a number of
years. To address this challenge, we significantly accelerate the
computation of the Gaussian Process (GP) for time series data. In the
context of a Change Point Detection (CPD) algorithm, we reduce its
computational complexity from O($n^{5}$) to O($n^{2}$). Our efficient
algorithm makes it possible to compute the Change Points using the
hourly price data from the California Electricity Crisis. By
comparing the detected Change Points with known events, we show that
the Change Point Detection algorithm is indeed effective in detecting
signals preceding major events.