Forecasting large collections of time series: feature-based methods

• URL: http://arxiv.org/abs/2309.13807v1
• Date: Mon, 25 Sep 2023 01:23:02 GMT
• Title: Forecasting large collections of time series: feature-based methods
• Authors: Li Li, Feng Li, Yanfei Kang
• Abstract summary: When forecasting large collections of time series, two lines of approaches have been developed using time series features. This chapter discusses the state-of-the-art feature-based methods, with reference to open-source software implementations.
• Score: 7.353918137830393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In economics and many other forecasting domains, the real world problems are too complex for a single model that assumes a specific data generation process. The forecasting performance of different methods changes depending on the nature of the time series. When forecasting large collections of time series, two lines of approaches have been developed using time series features, namely feature-based model selection and feature-based model combination. This chapter discusses the state-of-the-art feature-based methods, with reference to open-source software implementations.

Related papers

• Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series [3.9599054392856483]
  Time series foundation models are pre-trained on large datasets and are able to achieve state-of-the-art performance in diverse tasks. We propose a prompt-tuning-inspired fine-tuning technique, Gen-P-Tuning, that enables us to adapt an existing univariate time series foundation model. We demonstrate the effectiveness of our fine-tuning approach against various baselines on two MIMIC classification tasks, and on influenza-like illness forecasting.
  arXiv  Detail & Related papers  (2024-11-19T19:20:58Z)
• Towards Long-Context Time Series Foundation Models [17.224575072056627]
  Time series foundation models have shown impressive performance on a variety of tasks, across a wide range of domains, even in zero-shot settings. This study bridges the gap by systematically comparing various context expansion techniques from both language and time series domains.
  arXiv  Detail & Related papers  (2024-09-20T14:19:59Z)
• Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
  Time series data is of great significance in real-world scenarios. Recent years have witnessed remarkable breakthroughs in the time series community. We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• Few-Shot Forecasting of Time-Series with Heterogeneous Channels [4.635820333232681]
  We develop a model composed of permutation-invariant deep set-blocks which incorporate a temporal embedding. We show through experiments that our model provides a good generalization, outperforming baselines carried over from simpler scenarios.
  arXiv  Detail & Related papers  (2022-04-07T14:02:15Z)
• 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)
• 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)
• Novel Features for Time Series Analysis: A Complex Networks Approach [62.997667081978825]
  Time series data are ubiquitous in several domains as climate, economics and health care. Recent conceptual approach relies on time series mapping to complex networks. Network analysis can be used to characterize different types of time series.
  arXiv  Detail & Related papers  (2021-10-11T13:46:28Z)
• Merlion: A Machine Learning Library for Time Series [73.46386700728577]
  Merlion is an open-source machine learning library for time series. It features a unified interface for models and datasets for anomaly detection and forecasting. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a model in production.
  arXiv  Detail & Related papers  (2021-09-20T02:03:43Z)
• Instance-wise Graph-based Framework for Multivariate Time Series Forecasting [69.38716332931986]
  We propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps. The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast.
  arXiv  Detail & Related papers  (2021-09-14T07:38:35Z)
• Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case [2.997238772148965]
  Time series data are prevalent in many scientific and engineering disciplines. We present a new approach to time series forecasting using Transformer-based machine learning models. We show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.
  arXiv  Detail & Related papers  (2020-01-23T00:22:22Z)

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.