通过时间频率融合对比时间序列表示学习
1International School, Beijing University of Posts and Telecommunications, Beijing, China.
Frontiers in artificial intelligence
|June 27, 2024
概括
本研究介绍了时间-频率融合对比 (TF-FC),这是一个新的自我监督学习框架,用于未标记的时间序列数据. TF-FC通过结合时间和频率域增强来增强表示学习,显著提高识别精度.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 信号处理 信号处理
背景情况:
- 标记大型时间序列数据集是昂贵和耗时的.
- 从未标记的时间序列数据中进行有效的表示学习是一个重大挑战.
- 对比式学习提供了一种有希望的方法,用于从未标记的数据中获取表示.
研究的目的:
- 提出一个自主监督的时间序列表示学习框架,使用时频融合对比 (TF-FC).
- 通过利用时间和频率领域,从未标记的数据中学习有效的时间序列表示.
- 提高时间序列分类模型的区分能力.
主要方法:
- 开发了一个框架,将时间域和频域增强相结合,用于多样化的样本生成.
- 时间域增强包括动,缩放,排列和掩盖.
- 频域增强涉及快速里埃转换 (FFT),然后进行过,频率操纵和相位移动,并使用内核PCA进行融合.
主要成果:
- TF-FC框架有效地通过捕获时间和频率域特征来提取信息特征.
- 对SleepEEG,HAR,手势和数据集的实验表明,识别准确度有显著的改善.
- 在时间序列表示学习中,TF-FC的表现优于其他最先进的方法 (SOTA).
结论:
- TF-FC提供了一种强大的自我监督方法,用于从未标记的时间序列数据中学习表示.
- 时间和频率域增强的融合是提高特征提取和模型性能的关键.
- 这种方法为各种领域的时间序列分析提供了具有成本效益和效率的解决方案.
相关概念视频
01:11Continuous -time Fourier Transform
310
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
310
01:26Linear Approximation in Frequency Domain
89
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
89
01:26Discrete-time Fourier transform
300
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
One of the notable...
300
00:54Time-Series Graph
4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
01:20Discrete-Time Fourier Series
252
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
252
01:15Discrete Fourier Transform
255
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
255