Wrangle 2016: Seeing Behaviors as Humans Do: Uncovering Hidden Patterns in Time-Series Data with Deep Networks
By Mohammad Saffar, Arimo Time-series (longitudinal) data occurs in nearly every aspect of our lives; including customer activity on a website, financial transactions, sensor/IoT data. Just like in written text, specific events in a sequence of events are affected by the past and affect events in the future, and this can reveal a lot of hidden structure in the source of the events. Yet, today's predictive techniques largely rely on demographic (cross-sectional) data and do not take into account the sequences of events as they occur. In this session, Mohammad will discuss techniques for taking time-series data from a variety of domains and sources and grouping entities based on temporal behavior, using RNNs. These clusters of time-series sequences can either be visualized or used for campaign targeting in the case of user clickstream behavior or understanding stock symbols that behave similarly based on their trading behavior.
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Wrangle 2016: Seeing Behaviors as Humans Do: Uncovering Hidden Patterns in Time-Series Data with Deep Networks
- 1. © 2016 Cloudera, Inc. All rights reserved. 1
- 2. © 2016 Cloudera, Inc. All rights reserved. 2 Seeing Behaviors as (Some) Humans Do! Uncovering Hidden Patterns in Time- Series Data with Deep Networks Mohammad Saffar, Deep Learning SWE, Arimo Inc.
- 3. © 2016 Cloudera, Inc. All rights reserved. 3 Time-Series Are Everywhere! • Data Sources: • Clickstream • IoT / Sensors • Genomics data • Customer History • Financial Transactions • Stock Markets • Applicable Problems: • Customer Segmentation • Anomaly Detection • Predictive Maintenance • User Modeling • Portfolio Optimization • 1:1 marketing
- 4. © 2016 Cloudera, Inc. All rights reserved. 4 Just a Little History … Features are key!
- 5. © 2016 Cloudera, Inc. All rights reserved. 5 The Power of Deep Learning! Let system learn a rich feature pool!
- 6. © 2016 Cloudera, Inc. All rights reserved. 6 Previous Approaches: • Collapse rows • Compute aggregate features • Feed into predictive model Problems: • Hand-picked features • Throwing away valuable data Our Approach: • Use all observations (no aggregation) • Ingest variable length sequences • Let models decide what to remember Outcome: • Optimized learned features • Embeddings! How to Process Time-Series Data?
- 7. © 2016 Cloudera, Inc. All rights reserved. 7 Recurrent Deep Neural Networks Input Recurrent Network Predictive Network Embeddings Clustering Salient Features
- 8. © 2016 Cloudera, Inc. All rights reserved. 8 Customer Segmentation Hadoop Storage Apache Spark Distributed TensorFlow
- 9. © 2016 Cloudera, Inc. All rights reserved. 9 Results
- 10. © 2016 Cloudera, Inc. All rights reserved. 10 What We Do • Predictive Behavioral Intelligence • Deep Learning on Time-Series! • Use Cases: • Automated customer segmentation based on clickstream analysis and behavioral patterns. • Behavioral-based demand prediction to better optimize inventory, grow same store sales, and enhance customer experience. • Automated incentive engine to create individualized marketing programs for abandoned carts or high propensity customers. • Time-series-based anomaly & fraud detection by modeling common patterns in a stream of individual users’ activities and detecting outliers.
- 11. Thank you.