FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data

• URL: http://arxiv.org/abs/2101.03298v2
• Date: Sat, 16 Jan 2021 00:46:04 GMT
• Title: FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
• Authors: Shuyuan Yan, Bolin Ding, Wei Guo, Jingren Zhou, Zhewei Wei, Xiaowei Jiang, and Sheng Xu
• Abstract summary: Real-time forecasting can be conducted in two steps: first, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples.
• Score: 31.29499654765994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecasting pipeline with large volumes of high-dimensional time series data. Real-time forecasting can be conducted in two steps. First, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. Second, a forecasting model is trained on the aggregated results to predict the trend of the specified measure. While there are a number of forecasting models available, the first step is the performance bottleneck. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples. We introduce how to construct compact GSW samples with the existence of multiple measures to be analyzed. We conduct experiments to evaluate our solution and compare it with alternatives on real data.

Related papers

• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• DAM: Towards A Foundation Model for Time Series Forecasting [0.8231118867997028]
  We propose a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time. It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output; and (3) the basis coefficients of a continuous function of time.
  arXiv  Detail & Related papers  (2024-07-25T08:48:07Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization [0.0]
  It is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. We propose a framework for forecasting short high-dimensional time series data by combining low-rank temporal matrix factorization and optimal model selection on latent time series.
  arXiv  Detail & Related papers  (2021-12-15T11:39:21Z)
• Meta-Forecasting by combining Global DeepRepresentations with Local Adaptation [12.747008878068314]
  We introduce a novel forecasting method called Meta Global-Local Auto-Regression (Meta-GLAR) It adapts to each time series by learning in closed-form the mapping from the representations produced by a recurrent neural network (RNN) to one-step-ahead forecasts. Our method is competitive with the state-of-the-art in out-of-sample forecasting accuracy reported in earlier work.
  arXiv  Detail & Related papers  (2021-11-05T11:45:02Z)
• Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
  We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T20:41:19Z)
• Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
  Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
  arXiv  Detail & Related papers  (2021-09-01T09:52:31Z)
• Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves [0.0]
  We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series. We propose using late data fusion, using a stacked ensemble of two forecasting models and two meta-features that prove their predictive power during a preliminary forecasting stage.
  arXiv  Detail & Related papers  (2021-08-19T14:44:46Z)
• Time series forecasting based on complex network in weighted node similarity [12.246860992135783]
  In time series analysis, visibility graph theory transforms time series data into a network model. The node similarity index is used as the weight coefficient to optimize the prediction algorithm. The method has more accurate forecasting ability and can provide more accurate forecasts in the field of time series and actual scenes.
  arXiv  Detail & Related papers  (2021-03-14T01:01:41Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting [106.3504366501894]
  Self-driving vehicles and robotic manipulation systems often forecast future object poses by first detecting and tracking objects. This detect-then-forecast pipeline is expensive to scale, as pose forecasting algorithms typically require labeled sequences of object poses. We propose to first forecast 3D sensor data and then detect/track objects on the predicted point cloud sequences to obtain future poses. This makes it less expensive to scale pose forecasting, as the sensor data forecasting task requires no labels.
  arXiv  Detail & Related papers  (2020-03-18T17:54:28Z)

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.