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Schedule: Temporal data and time-series sessions
9:00am - 5:00pmMonday, April 15 & Tuesday, April 16
Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.
Time series database (TSDB) is of great use for data management in IoT, finance, etc. Performance is always a major optimization point for TSDB. Recently, we introduced neural networks and reinforcement learning to perform mode selection for compression algorithm. Experimental results show one can improve average compression ratio by 20%-120%, comparing with other well-known compression format.
In this talk we shall shares a novel two-step approach for building more reliable prediction models by integrating anomalies in them. Further, we shall walk the audience through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data.
Our firm focuses on the application of AI to investment management. Topics covered in this presentation include the application of AI to the problem of asset selection, dealing with low signal-to-noise ratios in financial time series data, the development of real-time macroeconomic indicators from social media data, and the use of heterogeneous compute architectures, specifically GPUs and FPGAs.