The detection of recurring behavioral patterns in time series data, also called motif discovery, is a crucial step for mining insights complex especially environments where manual monitoring not feasible. However, current state-of-the-art algorithms fall short their applicability production (due to static length, lots user defined parameters, only providing the best pair, etc.). In this paper, ...

Research on forecasting methods of time series data has become one the hot spots. More and more are produced in various fields. It provides for research analysis method, promotes development research. Due to generation highly complex large-scale data, construction models brings greater challenges. The main challenges modeling high complexity low accuracy poor generalization ability prediction m...

Most of today’s time series data contain anomalies and multiple seasonalities, accurate anomaly detection in these is critical to almost any type business. However, most mainstream forecasting models used for can only incorporate one or no seasonal component into their forecasts cannot capture every known pattern data. In this paper, we propose a new multi-seasonal model that extends the popula...