auto-sktime: Automated Time Series Forecasting

• URL: http://arxiv.org/abs/2312.08528v3
• Date: Tue, 30 Apr 2024 15:23:59 GMT
• Title: auto-sktime: Automated Time Series Forecasting
• Authors: Marc-André Zöller, Marius Lindauer, Marco F. Huber,
• Abstract summary: We introduce auto-sktime, a novel framework for automated time series forecasting. The proposed framework uses the power of automated machine learning (AutoML) techniques to automate the creation of the entire forecasting pipeline. Experimental results on 64 diverse real-world time series datasets demonstrate the effectiveness and efficiency of the framework.
• Score: 18.640815949661903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In today's data-driven landscape, time series forecasting is pivotal in decision-making across various sectors. Yet, the proliferation of more diverse time series data, coupled with the expanding landscape of available forecasting methods, poses significant challenges for forecasters. To meet the growing demand for efficient forecasting, we introduce auto-sktime, a novel framework for automated time series forecasting. The proposed framework uses the power of automated machine learning (AutoML) techniques to automate the creation of the entire forecasting pipeline. The framework employs Bayesian optimization, to automatically construct pipelines from statistical, machine learning (ML) and deep neural network (DNN) models. Furthermore, we propose three essential improvements to adapt AutoML to time series data. First, pipeline templates to account for the different supported forecasting models. Second, a novel warm-starting technique to start the optimization from prior optimization runs. Third, we adapt multi-fidelity optimizations to make them applicable to a search space containing statistical, ML and DNN models. Experimental results on 64 diverse real-world time series datasets demonstrate the effectiveness and efficiency of the framework, outperforming traditional methods while requiring minimal human involvement.

Related papers

• Empowering Time Series Forecasting with LLM-Agents [23.937463131291974]
  We propose DCATS, a Data-Centric Agent for Time Series.<n>We evaluate DCATS using four time series forecasting models on a large-scale traffic volume forecasting dataset.
  arXiv  Detail & Related papers  (2025-08-06T09:14:08Z)
• Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting [64.45587649141842]
  Time-series forecasting plays a critical role in many real-world applications.<n>No single model consistently outperforms others across different test samples, but instead (ii) each model excels in specific cases.<n>We introduce TimeFuse, a framework for collective time-series forecasting with sample-level adaptive fusion of heterogeneous models.
  arXiv  Detail & Related papers  (2025-05-24T00:45:07Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• FlowScope: Enhancing Decision Making by Time Series Forecasting based on Prediction Optimization using HybridFlow Forecast Framework [0.0]
  Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare, and finance. We propose FlowScope which offers a versatile and robust platform for predicting time series data. This empowers enterprises to make informed decisions and optimize long-term strategies for maximum performance.
  arXiv  Detail & Related papers  (2024-11-16T06:25:30Z)
• Fully Automated Correlated Time Series Forecasting in Minutes [31.198713853170375]
  We propose a fully automated and highly efficient correlated time series forecasting framework. It includes a data-driven, iterative strategy to automatically prune a large search space to obtain a high-quality search space for a new forecasting task. Experiments on seven benchmark datasets offer evidence that the framework is capable of state-of-the-art accuracy and is much more efficient than existing methods.
  arXiv  Detail & Related papers  (2024-11-06T09:02:13Z)
• Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models [79.84205827056907]
  We present Self-Steering Optimization ($SSO$), an algorithm that autonomously generates high-quality preference data.<n>$SSO$ employs a specialized optimization objective to build a data generator from the policy model itself, which is used to produce accurate and on-policy data.<n>Our evaluation shows that $SSO$ consistently outperforms baselines in human preference alignment and reward optimization.
  arXiv  Detail & Related papers  (2024-10-22T16:04:03Z)
• AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting [80.14147131520556]
  AutoGluon-TimeSeries is an open-source AutoML library for probabilistic time series forecasting. It generates accurate point and quantile forecasts with just 3 lines of Python code.
  arXiv  Detail & Related papers  (2023-08-10T13:28:59Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Efficient Automated Deep Learning for Time Series Forecasting [42.47842694670572]
  We propose an efficient approach for the joint optimization of neural architecture and hyperparameters of the entire data processing pipeline for time series forecasting. In contrast to common NAS search spaces, we designed a novel neural architecture search space covering various state-of-the-art architectures. We empirically study several different budget types enabling efficient multi-fidelity optimization on different forecasting datasets.
  arXiv  Detail & Related papers  (2022-05-11T14:03:25Z)
• Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [68.86835407617778]
  Autoformer is a novel decomposition architecture with an Auto-Correlation mechanism. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a relative improvement on six benchmarks.
  arXiv  Detail & Related papers  (2021-06-24T13:43:43Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• AutoAI-TS: AutoAI for Time Series Forecasting [14.078195334596494]
  We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf) system. AutoAI-TS automatically performs all the data preparation, model creation, parameter optimization, training and model selection for users. It then evaluates and ranks pipelines using the proposed T-Daub mechanism to choose the best pipeline.
  arXiv  Detail & Related papers  (2021-02-24T15:30:54Z)
• Automatic deep learning for trend prediction in time series data [0.0]
  Deep Neural Network (DNN) algorithms have been explored for predicting trends in time series data. In many real world applications, time series data are captured from dynamic systems. We show how a recent AutoML tool can be effectively used to automate the model development process.
  arXiv  Detail & Related papers  (2020-09-17T19:47:05Z)
• Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation [97.42894942391575]
  We propose FAST-DAD to distill arbitrarily complex ensemble predictors into individual models like boosted trees, random forests, and deep networks. Our individual distilled models are over 10x faster and more accurate than ensemble predictors produced by AutoML tools like H2O/AutoSklearn.
  arXiv  Detail & Related papers  (2020-06-25T09:57:47Z)

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.