The Relevance of AWS Chronos: An Evaluation of Standard Methods for Time Series Forecasting with Limited Tuning

• URL: http://arxiv.org/abs/2501.10216v1
• Date: Fri, 17 Jan 2025 14:23:54 GMT
• Title: The Relevance of AWS Chronos: An Evaluation of Standard Methods for Time Series Forecasting with Limited Tuning
• Authors: Matthew Baron, Alex Karpinski,
• Abstract summary: Chronos is a transformer-based time series forecasting framework. Our analysis reveals that while Chronos demonstrates superior performance for longer-term predictions, traditional models show significant degradation as context length increases. This study provides a case for deploying Chronos in real-world applications where limited model tuning is feasible.
• Score: 0.0
• License:
• Abstract: A systematic comparison of Chronos, a transformer-based time series forecasting framework, against traditional approaches including ARIMA and Prophet. We evaluate these models across multiple time horizons and user categories, with a focus on the impact of historical context length. Our analysis reveals that while Chronos demonstrates superior performance for longer-term predictions and maintains accuracy with increased context, traditional models show significant degradation as context length increases. We find that prediction quality varies systematically between user classes, suggesting that underlying behavior patterns always influence model performance. This study provides a case for deploying Chronos in real-world applications where limited model tuning is feasible, especially in scenarios requiring longer prediction.

Related papers

• Deconfounding Time Series Forecasting [1.5967186772129907]
  Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current observations of variables to predict future outcomes. We propose an enhanced forecasting approach that incorporates representations of latent confounders derived from historical data.
  arXiv  Detail & Related papers  (2024-10-27T12:45:42Z)
• Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting [65.40983982856056]
  We introduce STOIC, that leverages correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides 16% more accurate and better-calibrated forecasts.
  arXiv  Detail & Related papers  (2024-07-02T20:14:32Z)
• Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting [5.354055742467354]
  Low-Rank Adaptation (LoRA) is a technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. This paper examines the impact of LoRA on contemporary time series foundational models: Lag-Llama, MOIRAI, and Chronos.
  arXiv  Detail & Related papers  (2024-05-16T16:05:33Z)
• Chronos: Learning the Language of Time Series [79.38691251254173]
  Chronos is a framework for pretrained probabilistic time series models. We show that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks.
  arXiv  Detail & Related papers  (2024-03-12T16:53:54Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Counterfactual Explanations for Time Series Forecasting [14.03870816983583]
  We formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF. ForecastCF solves the problem by applying gradient-based perturbations to the original time series. Our results show that ForecastCF outperforms the baseline in terms of counterfactual validity and data manifold closeness.
  arXiv  Detail & Related papers  (2023-10-12T08:51:59Z)
• 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)
• Model-Attentive Ensemble Learning for Sequence Modeling [86.4785354333566]
  We present Model-Attentive Ensemble learning for Sequence modeling (MAES) MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.
  arXiv  Detail & Related papers  (2021-02-23T05:23:35Z)

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.