Time-series prediction with neural networks
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This document discusses approaches for time-series prediction using neural networks and analyzing customer order data from Instacart. It describes using word embedding models like word2vec to predict future purchases based on past order patterns and explore relationships between product purchases over time. It also emphasizes the importance of joining time-series data with metadata in a database to enable richer predictive queries and analysis. Time-series databases are presented as a solution for scaling to large volumes of IoT and sensor data and providing efficiencies over traditional databases by treating time as a first-class citizen.
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Time-series prediction with neural networks
- 1. Time-series Prediction with Neural Networks Rudradeb Mitra | rudra@timescale.com
- 3. How?
- 4. Analyzing 3M Instacart Orders • Prior: ~3.2m orders • Train:~131k orders • Test: ~75k orders • Unsupervised learning: word2vec
- 5. Instacart Data product_id user_i d eval_set order_number order_dow order_hour_of_day days_since_prior_order 2539329 1 prior 1 2 8 2398795 1 prior 2 5 7 15 473747 1 prior 3 7 12 20 22544786 1 prior 4 1 7 21 4215438 1 prior 5 3 15 28 2295261 1 prior 6 2 7 19 2295261 1 prior 7 6 20 20 2550362 1 prior 8 5 14 14 1187899 1 prior 9 2 16 0 2168274 1 prior 10 2 8 30 1501582 1 train 11 1 11 10
- 6. One approach to Prediction word2vec Hey, you are What doing Input words Predicted next word
- 7. Germany Poland Warsaw Berlin Vector distance ~ semantic distance Berlin - Warsaw + Poland = Warsaw One approach to Prediction Berlin is the capital of Germany Warsaw is the capital of ?
- 8. One approach to Prediction 2539329 2398785 473747 2550368 1187899 word2vec (Product ID) (Predicted product ID)
- 9. product 1 product 3 product 4 product 2 Product 1 and Product 3 are ordered together. So are Product 2 and Product 4. So if Product 1, Product 2 and Product 3 are ordered then you can product Product 4 will be ordered One approach to Prediction
- 10. What is missing?
- 11. Instacart Data product_id user_i d eval_set order_number order_dow order_hour_of_day days_since_prior_order 2539329 1 prior 1 2 8 2398795 1 prior 2 5 7 15 473747 1 prior 3 7 12 20 22544786 1 prior 4 1 7 21 4215438 1 prior 5 3 15 28 2295261 1 prior 6 2 7 19 2295261 1 prior 7 6 20 20 2550362 1 prior 8 5 14 14 1187899 1 prior 9 2 16 0 2168274 1 prior 10 2 8 30 1501582 1 train 11 1 11 10
- 13. How can time-series data be used in making predictions?
- 14. 2539329, {1,8,} 2550368, {4,12} 473747, {6,21} 2398785, {7,15} 1187899, {4,14} (Product ID, {order_dow, order_hour}) Observations Predictions Second approach to Prediction
- 15. What else is missing?
- 16. Richer Analysis 1. Perform richer analysis - Query subset of data – Model output: user_id will buy product_id at time_t – Query: List users who will buy product at time_t 3. Ongoing maintenance - Can help with monitoring models 2. Avoid data silos - JOIN time-series data with relational metadata —Model output: user_id will buy product_id at time_t —Query: Find stores where user_id will buy at time_t
- 17. You need a database
- 18. Application EngineDatabase Learning Algorithm Perform complex queries Predictions Predictive Analytics architecture
- 19. What other applications are there?
- 21. IoT Sensor data, machine data, industrial monitoring, smart home, wearables
- 22. Events Clickstreams, application events, outages, errors, system status
- 24. Application EngineDatabase Learning Algorithm Perform complex queries Predictions Predictive Analytics architecture
- 25. 25GB data collected per hour by connected cars (McKinsey) “Our Boeing 787s generate half a terabyte of data per flight” - Virgin Atlantic IT director Problem: Time-series data piles up very quickly
- 27. Time-series databases introduce efficiencies by treating time as a first-class citizen
- 28. Time-series databases fastest growing DB category Source: DB Engines
- 29. SQL made scalable for time-series data Packaged as a PostgreSQL extension
- 30. Timescale vs PostgreSQL Postgres 9.6.2 on Azure standard DS4 v2 (8 cores), SSD (premium LRS storage) Each row has 12 columns (1 timestamp, indexed 1 host ID, 10 metrics) 144K metrics/s 14.4K inserts/s
- 31. Postgres 9.6.2 on Azure standard DS4 v2 (8 cores), SSD (premium LRS storage) Each row has 12 columns (1 timestamp, indexed 1 host ID, 10 metrics) Timescale vs PostgreSQL 1.3M metrics/s 130K inserts/s 15x
- 32. Open-source release in March https://github.com/timescale/timescaledb Want to build some cool demo’s? Contact me rudra@timescale.com