On Wednesday the average cost for a gallon of regular gas in Los Angeles reached $6.08, leaping 2.3 cents overnight and breaking a record set earlier this year, according to the latest data from AAA. Los Angeles is not alone in its pain as the cost of gas spikes across the nation. And according to analysts, the switch to a more expensive summer blend for other parts of the country promises the hurt will not stop anytime soon. The average cost for regular gas is more than $4 for nearly every state. According to AAA, the national average is $4.56, but California leads the nation with an average of $6.05.
Experts say a perfect storm of supply-and-demand issues are sending gas prices in Los Angeles soaring again, with the price-per-gallon increasing more than 14 cents in the last 16 days, according to the latest fuel prices tracked by AAA. L.A. fuel prices are again inching toward a $6-a-gallon record set in March. The average price of a gallon of regular gasoline in the Los Angeles area is currently $5.91, with plenty of stations charging well over that. A year ago the price was $4.16. Overnight, the price jumped 2.2 cents, the highest level it has risen since February.
Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile by Chin-Chia Michael Yeh Doctor of Philosophy, Graduate Program in Computer Science University of California, Riverside, September 2018 Dr. Eamonn Keogh, Chairperson The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subsequences. In this thesis, we have introduced a near universal time series data mining tool called matrix profile which solves the all-pairssimilarity-search problem and caches the output in an easy-to-access fashion. The proposed algorithm is not only parameter-free, exact and scalable, but also applicable for both single and multidimensional time series. By building time series data mining methods on top of matrix profile, many time series data mining tasks (e.g., motif discovery, discord discovery, shapelet discovery, semantic segmentation, and clustering) can be efficiently solved. Because the same matrix profile can be shared by a diverse set of time series data mining methods, matrix profile is versatile and computed-once-use-many-times data structure. We demonstrate the utility of matrix profile for many time series data mining problems, including motif discovery, discord discovery, weakly labeled time series classification, and vi representation learning on domains as diverse as seismology, entomology, music processing, bioinformatics, human activity monitoring, electrical power-demand monitoring, and medicine. We hope the matrix profile is not the end but the beginning of many more time series data mining projects.