Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction with a probabilistic formulation for uncertainty estimation. However, such models are hard to tune, scale, and add exogenous variables to. Motivated by the recent resurgence of Long Short Term Memory networks, we propose a novel end-to-end Bayesian deep model that provides time series prediction along with uncertainty estimation. We provide detailed experiments of the proposed solution on completed trips data, and successfully apply it to large-scale time series anomaly detection at Uber.Comment: To appear in DSBDA-2017 @ ICDM'1

No abstract

No abstract

Approximate results based on samples often provide the only way in which advanced analytical applications on very massive data sets can satisfy their time and resource constraints. Unfortunately, methods and tools for the computation of accurate early results are currently not supported in MapReduce-oriented systems although these are intended for 'big data'. Therefore, we proposed and implemented a non-parametric extension of Hadoop which allows the incremental computation of early results for arbitrary workflows, along with reliable on-line estimates of the degree of accuracy achieved so far in the computation. These estimates are based on a technique called bootstrapping that has been widely employed in statistics and can be applied to arbitrary functions and data distributions. In this paper, we describe our Early Accurate Result Library (EARL) for Hadoop that was designed to minimize the changes required to the MapReduce framework. Various tests of EARL of Hadoop are presented to characterize the frequent situations where EARL can provide major speed-ups over the current version of Hadoop.

Abstract-The problem of supporting data mining applications proved to be difficult for database management systems and it is now proving to be very challenging for data stream management systems (DSMSs), where the limitations of SQL are made even more severe by the requirements of continuous queries. The major technical advances that achieved separately on DSMSs and on data stream mining algorithms have failed to converge and produce powerful data stream mining systems. Such systems, however, are essential since the traditional pullbased approach of cache mining is no longer applicable, and the push-based computing mode of data streams and their bursty traffic complicate application development. For instance, to write mining applications with quality of service (QoS) levels approaching those of DSMSs, a mining analyst would have to contend with many arduous tasks, such as support for data buffering, complex storage and retrieval methods, scheduling, fault-tolerance, synopsis-management, load shedding, and query optimization. Our Stream Mill Miner (SMM) system solves these problems by providing a data stream mining workbench that combines the ease of specifying high-level mining tasks, as in Weka, with the performance and QoS guarantees of a DSMS. This is accomplished in three main steps. The first is an open and extensible DSMS architecture where KDD queries can be easily expressed as user-defined aggregates (UDAs)-our system combines that with the efficiency of synoptic data structures and mining-aware load shedding and optimizations. The second key component of SMM is its integrated library of fast mining algorithms that are light enough to be effective on data streams. The third advanced feature of SMM is a Mining Model Definition Language (MMDL) that allows users to define the flow of mining tasks, integrated with a simple box&arrow GUI, to shield the mining analyst from the complexities of lower-level queries. SMM is the first DSMS capable of online mining and this paper describes its architecture, design, and performance on mining queries.

Web traffic represents a powerful mirror for various real-world phenomena. For example, it was shown that web search volumes have a positive correlation with stock trading volumes and with the sentiment of investors. Our hypothesis is that user browsing behavior on a domain-specific portal is a better predictor of user intent than web searches.We focus on the financial domain and we analyze the web browsing and trading data of more than 2600 stocks traded on NYSE, Nasdaq and SNP. The web browsing data consist of user page views related to stocks on Yahoo Finance, while the trading data include the trading volume of these stocks. We study the correlation and causality between web browsing and trading data while varying the time granularity (hourly, daily) and financial segmentation (individual tickers, industries, sectors).We find that web browsing on Yahoo Finance can anticipate stock trading volumes by two or three days, resulting in a higher predictive power than previous work that used web searches to predict trading volume. We also observe that grouping stocks into industries or sectors decreases the predictive power, whereas moving from hourly to daily time series granularity improves predictive power. We corroborate our findings with a theoretical intuition and extensive statistical and causality tests.

This study presents a field experiment in which media articles for a random sample of firms with earnings announcements are promoted to a one percent subset of Yahoo Finance users. Promoted firms have higher abnormal returns and some evidence of lower bid-ask spreads on the day of the earnings announcement. These results are more pronounced for less visible firms, negative earnings news, and on days with fewer promoted firms. These findings suggest that investor attention affects the pricing of earnings and that retail investors buy stocks that catch their attention, in a setting where attention is randomly assigned.

We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferation of time series data, explainable forecasting remains a challenging task for business and operational decision making. Hybrid solutions are needed to bridge the gap between interpretable classical methods and scalable deep learning models. We view Prophet as a precursor to such a solution. However, Prophet lacks local context, which is essential for forecasting the near-term future and is challenging to extend due to its Stan backend.NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework. Local context is introduced with auto-regression and covariate modules, which can be configured as classical linear regression or as Neural Networks. Otherwise, NeuralProphet retains the design philosophy of Prophet and provides the same basic model components.Our results demonstrate that NeuralProphet produces interpretable forecast components of equivalent or superior quality to Prophet on a set of generated time series. NeuralProphet outperforms Prophet on a diverse collection of real-world datasets. For short to medium-term forecasts, NeuralProphet improves forecast accuracy by 55 to 92 percent.