tsMorph: generation of semi-synthetic time series to understand algorithm performance

• URL: http://arxiv.org/abs/2312.01344v1
• Date: Sun, 3 Dec 2023 10:40:07 GMT
• Title: tsMorph: generation of semi-synthetic time series to understand algorithm performance
• Authors: Mois\'es Santos and Andr\'e de Carvalho and Carlos Soares
• Abstract summary: We present tsMorph, a straightforward approach for generating semi-synthetic time series through dataset morphing. In this paper, we demonstrate the utility of tsMorph by assessing the performance of the Long Short-Term Memory Network forecasting algorithm.
• Score: 0.4419843514606336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting is a subject of significant scientific and industrial importance. Despite the widespread utilization of forecasting methods, there is a dearth of research aimed at comprehending the conditions under which these methods yield favorable or unfavorable performances. Empirical studies, although common, encounter challenges due to the limited availability of datasets, impeding the extraction of reliable insights. To address this, we present tsMorph, a straightforward approach for generating semi-synthetic time series through dataset morphing. tsMorph operates by creating a sequence of datasets derived from two original datasets. These newly generated datasets exhibit a progressive departure from the characteristics of one dataset and a convergence toward the attributes of the other. This method provides a valuable alternative for obtaining substantial datasets. In this paper, we demonstrate the utility of tsMorph by assessing the performance of the Long Short-Term Memory Network forecasting algorithm. The time series under examination are sourced from the NN5 Competition. The findings reveal compelling insights. Notably, the performance of the Long Short-Term Memory Network improves proportionally with the frequency of the time series. These experiments affirm that tsMorph serves as an effective tool for gaining an understanding of forecasting algorithm behaviors, offering a pathway to overcome the limitations posed by empirical studies and enabling more extensive and reliable experimentation.

Related papers

• Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization [74.3339999119713]
  We develop a wavelet-based tokenizer that allows models to learn complex representations directly in the space of time-localized frequencies. Our method first scales and decomposes the input time series, then thresholds and quantizes the wavelet coefficients, and finally pre-trains an autoregressive model to forecast coefficients for the forecast horizon.
  arXiv  Detail & Related papers  (2024-12-06T18:22:59Z)
• Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting [15.431513584239047]
  Time series forecasting is critical in numerous real-world applications. Traditional forecasting techniques struggle when data is scarce or not available at all. Recent advancements often leverage large-scale foundation models for such tasks.
  arXiv  Detail & Related papers  (2024-11-24T07:44:39Z)
• TSI-Bench: Benchmarking Time Series Imputation [52.27004336123575]
  TSI-Bench is a comprehensive benchmark suite for time series imputation utilizing deep learning techniques. The TSI-Bench pipeline standardizes experimental settings to enable fair evaluation of imputation algorithms. TSI-Bench innovatively provides a systematic paradigm to tailor time series forecasting algorithms for imputation purposes.
  arXiv  Detail & Related papers  (2024-06-18T16:07:33Z)
• State Sequences Prediction via Fourier Transform for Representation Learning [111.82376793413746]
  We propose State Sequences Prediction via Fourier Transform (SPF), a novel method for learning expressive representations efficiently. We theoretically analyze the existence of structural information in state sequences, which is closely related to policy performance and signal regularity. Experiments demonstrate that the proposed method outperforms several state-of-the-art algorithms in terms of both sample efficiency and performance.
  arXiv  Detail & Related papers  (2023-10-24T14:47:02Z)
• 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)
• MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting [13.790498420659636]
  Time series forecasting has received wide interest from existing research due to its broad applications inherent challenging. This paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable.
  arXiv  Detail & Related papers  (2023-07-13T13:16:01Z)
• 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)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction [9.449017120452675]
  Time series is a special type of sequence data, a set of observations collected at even intervals of time and ordered chronologically. Existing deep learning techniques use generic sequence models for time series analysis, which ignore some of its unique properties. We propose a novel neural network architecture and apply it for the time series forecasting problem, wherein we conduct sample convolution and interaction at multiple resolutions for temporal modeling.
  arXiv  Detail & Related papers  (2021-06-17T08:15:04Z)
• Towards Synthetic Multivariate Time Series Generation for Flare Forecasting [5.098461305284216]
  One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest. In this study, we explore the usefulness of the conditional generative adversarial network (CGAN) as a means to perform data-informed oversampling.
  arXiv  Detail & Related papers  (2021-05-16T22:23:23Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)

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.