Greykite: Deploying Flexible Forecasting at Scale at LinkedIn

• URL: http://arxiv.org/abs/2207.07788v1
• Date: Fri, 15 Jul 2022 23:57:10 GMT
• Title: Greykite: Deploying Flexible Forecasting at Scale at LinkedIn
• Abstract summary: At LinkedIn, product owners use forecasts to set business targets, track outlook, and monitor health. We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn. Our benchmark results show excellent out-of-the-box speed and accuracy on datasets from a variety of domains.
• Score: 11.769823455954599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasts help businesses allocate resources and achieve objectives. At LinkedIn, product owners use forecasts to set business targets, track outlook, and monitor health. Engineers use forecasts to efficiently provision hardware. Developing a forecasting solution to meet these needs requires accurate and interpretable forecasts on diverse time series with sub-hourly to quarterly frequencies. We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn. Its flagship algorithm, Silverkite, provides interpretable, fast, and highly flexible univariate forecasts that capture effects such as time-varying growth and seasonality, autocorrelation, holidays, and regressors. The library enables self-serve accuracy and trust by facilitating data exploration, model configuration, execution, and interpretation. Our benchmark results show excellent out-of-the-box speed and accuracy on datasets from a variety of domains. Over the past two years, Greykite forecasts have been trusted by Finance, Engineering, and Product teams for resource planning and allocation, target setting and progress tracking, anomaly detection and root cause analysis. We expect Greykite to be useful to forecast practitioners with similar applications who need accurate, interpretable forecasts that capture complex dynamics common to time series related to human activity.

Related papers

• A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting [48.586399370741965]
  We present CastClaw, a forecasting system built through forecasting-oriented harness engineering.<n>CastClaw connects data, specialized models, analytical tools, user input, and a versioned execution record in one runtime.<n>CastClaw was validated offline on provincial electricity-load data from North China covering January--June 2026.
  arXiv  Detail & Related papers  (2026-08-31T15:36:41Z)
• CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting [74.29761960394848]
  We propose CastFlow, a dynamic agentic forecasting framework for large language models (LLMs)<n>CastFlow organizes the forecasting process into planning, action, forecasting, and reflection, establishing an agentic workflow.<n>Third, CastFlow adopts a role-specialized design that combines general-purpose reasoning with specialized numerical forecasting.
  arXiv  Detail & Related papers  (2026-04-30T13:24:42Z)
• Scaling Open-Ended Reasoning to Predict the Future [56.672065928345525]
  We train language models to make predictions on open-ended forecasting questions.<n>To scale up training data, we synthesize novel forecasting questions from global events reported in daily news.<n>We find calibration improvements from forecasting training generalize across popular benchmarks.
  arXiv  Detail & Related papers  (2025-12-31T18:59:51Z)
• ARIMA_PLUS: Large-scale, Accurate, Automatic and Interpretable In-Database Time Series Forecasting and Anomaly Detection in Google BigQuery [5.170341372465115]
  Time series forecasting and anomaly detection are common tasks for practitioners in industries such as retail, manufacturing, advertising and energy.<n>We present ARIMA_PLUS, a novel framework to overcome these two challenges by a unique combination of (a) accurate and interpretable time series models and (b) scalable and fully managed system infrastructure.
  arXiv  Detail & Related papers  (2025-10-28T14:18:50Z)
• Accuracy Law for the Future of Deep Time Series Forecasting [65.46625911002202]
  Time series forecasting inherently faces a non-zero error lower bound due to its partially observable and uncertain nature.<n>This paper focuses on a fundamental question: how to estimate the performance upper bound of deep time series forecasting.<n>Based on rigorous statistical tests of over 2,800 newly trained deep forecasters, we discover a significant exponential relationship between the minimum forecasting error of deep models and the complexity of window-wise series patterns.
  arXiv  Detail & Related papers  (2025-10-03T05:18:47Z)
• When Does Multimodality Lead to Better Time Series Forecasting? [96.26052272121615]
  We investigate whether and under what conditions such multimodal integration consistently yields gains.<n>Our findings reveal that the benefits of multimodality are highly condition-dependent.<n>Our study offers a rigorous, quantitative foundation for understanding when multimodality can be expected to aid forecasting tasks.
  arXiv  Detail & Related papers  (2025-06-20T23:55:56Z)
• OneForecast: A Universal Framework for Global and Regional Weather Forecasting [67.61381313555091]
  We propose a global-regional nested weather forecasting framework (OneForecast) based on graph neural networks.<n>By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region.<n>We introduce an adaptive messaging mechanism, using dynamic gating units, to deeply integrate node and edge features for more accurate extreme event forecasting.
  arXiv  Detail & Related papers  (2025-02-01T06:49:16Z)
• Neural Conformal Control for Time Series Forecasting [54.96087475179419]
  We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments. Our approach acts as a neural controller designed to achieve desired target coverage, leveraging auxiliary multi-view data with neural network encoders. We empirically demonstrate significant improvements in coverage and probabilistic accuracy, and find that our method is the only one that combines good calibration with consistency in prediction intervals.
  arXiv  Detail & Related papers  (2024-12-24T03:56:25Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a causal Transformer for unified time series forecasting. Based on large-scale pre-training, Timer-XL achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• Inter-Series Transformer: Attending to Products in Time Series Forecasting [5.459207333107234]
  We develop a new Transformer-based forecasting approach using a shared, multi-task per-time series network. We provide a case study applying our approach to successfully improve demand prediction for a medical device manufacturing company.
  arXiv  Detail & Related papers  (2024-08-07T16:22:21Z)
• Site-specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning [0.0]
  Recent developments in machine learning have prompted increasing interest in applying ML as a novel approach towards this problem. We develop a working ML framework, named 'Multi-SiteBoost' and initial testing results show a significant improvement compared with gridded values from bias-corrected NWP models.
  arXiv  Detail & Related papers  (2024-04-04T09:12:13Z)
• Scalable Spatiotemporal Prediction with Bayesian Neural Fields [3.3299088915999295]
  BayesNF integrates a deep neural network architecture for high-capacity estimation with hierarchical Bayesian inference for robust predictive uncertainty. BayesNF delivers improvements on prediction problems from climate and public health data containing tens of thousands of measurements.
  arXiv  Detail & Related papers  (2024-03-12T13:47:50Z)
• Scaling transformer neural networks for skillful and reliable medium-range weather forecasting [23.249955524044392]
  We introduce Stormer, a state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days.
  arXiv  Detail & Related papers  (2023-12-06T19:46:06Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain [54.67888148566323]
  We introduce three large-scale time series forecasting datasets from the cloud operations domain. We show it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method.
  arXiv  Detail & Related papers  (2023-10-08T08:09:51Z)
• Streaming Motion Forecasting for Autonomous Driving [71.7468645504988]
  We introduce a benchmark that queries future trajectories on streaming data and we refer to it as "streaming forecasting" Our benchmark inherently captures the disappearance and re-appearance of agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks. We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster.
  arXiv  Detail & Related papers  (2023-10-02T17:13:16Z)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect [3.830797055092574]
  How to handle time features shall be the core question of any time series forecasting model. In this paper, we rigorously analyze three prevalent but deficient/unfounded deep time series forecasting mechanisms. We propose a novel time series forecasting network, i.e. RTNet, on the basis of aforementioned analysis.
  arXiv  Detail & Related papers  (2022-07-22T08:34:31Z)
• Forecasting Future World Events with Neural Networks [68.43460909545063]
  Autocast is a dataset containing thousands of forecasting questions and an accompanying news corpus. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts. We test language models on our forecasting task and find that performance is far below a human expert baseline.
  arXiv  Detail & Related papers  (2022-06-30T17:59:14Z)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.