Results 1 to 10 of about 3,160,678 (291)

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics [PDF]

Statistical Analysis and Data Mining 2020, 2019
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable comparisons against alternative ...
arxiv   +1 more source

Volatility of Linear and Nonlinear Time Series [PDF]

, 2004
Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, $u_i$, can be detected and quantified by studying the correlations in the magnitude series $|u_i|$, i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact relation between the correlations ...
arxiv   +1 more source

Kolmogorov Space in Time Series Data [PDF]

, 2016
We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition.
arxiv   +1 more source

MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data [PDF]

arXiv, 2021
Time series forecasting is widely used in business intelligence, e.g., forecast stock market price, sales, and help the analysis of data trend. Most time series of interest are macroscopic time series that are aggregated from microscopic data. However, instead of directly modeling the macroscopic time series, rare literature studied the forecasting of ...
arxiv  

MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting [PDF]

arXiv, 2023
Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus of numerous studies.
arxiv  

Forecasting Hierarchical Time Series [PDF]

SDSS2022 Section, JSM Proceedings, 2022, pp. 675-680, 2022
This paper addresses a common problem with hierarchical time series. Time series analysis demands the series for a model to be the sum of multiple series at corresponding sub-levels. Hierarchical Time Series presents a two-fold problem. First, each individual time series model at each level in the hierarchy must be estimated separately.
arxiv  

SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets [PDF]

arXiv, 2023
Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. This paper proposes a Semi-
arxiv  

Triadic time series motifs [PDF]

EPL, 125 (2019) 18002, 2018
We introduce the concept of time series motifs for time series analysis. Time series motifs consider not only the spatial information of mutual visibility but also the temporal information of relative magnitude between the data points. We study the profiles of the six triadic time series.
arxiv   +1 more source

Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification [PDF]

arXiv, 2020
Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed ...
arxiv  

Fuzzy clustering of ordinal time series based on two novel distances with economic applications [PDF]

arXiv, 2023
Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete response. In this paper, the problem of clustering ordinal time series is addressed. To this aim, two novel distances between ordinal time series are
arxiv