对于多源,不规则采样和重叠时间序列的代转移分解算法
Colin O Quinn1,2, Ronald H Brown2,3, George F Corliss3
1Department of Computer Science, Marquette University, 1313 W. Wisconsin Avenue, Milwaukee, WI 53233, USA.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了代转移分类 (ISD) 算法,用于将低频传感器数据转换为高频时间序列. ISD算法通过将多个非均样本数据流分解为每日表示来提高预测的准确性.
科学领域:
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
- 信号处理 信号处理
背景情况:
- 准确的时间序列预测通常需要比可用的数据提供更高的时间分辨率.
- 现有的时间分解方法仅限于单个,均采样的时间序列,阻碍了现实世界的应用.
- 多源,非均采样的传感器数据流对传统的分类技术提出了挑战.
研究的目的:
- 引入代转移分类 (ISD) 算法,用于处理和分类多个,非均采样的传感器数据流.
- 为了将低频测量转化为单一,连贯的高频信号.
- 解决多源场景中现有的时间分解技术的局限性.
主要方法:
- 代转移分类 (ISD) 算法采用了一种代的,两相的过程.
- 第一个阶段:使用多重线性回归进行预测,从低频数据和相关变量生成高频序列.
- 第二阶段:更新阶段将低频观测重新分配到高频时期,代地改进估计.
主要成果:
- ISD成功地将多个,间隔不均的时间序列与重叠的间隔分解成一个单一的每日表示.
- 使用天然气数据的案例研究表明,与现有方法相比,有了显著的改进.
- 在计费周期数据方面实现了1.4-4.3%的WMAPE改进,在住宅数据方面实现了4.6-10.4%.
结论:
- ISD算法有效地将复杂的多源传感器数据分解为每日时间序列.
- 在处理不统一的数据时,ISD提供了一个强大的解决方案来提高时间序列预测的准确性.
- 该方法在现实世界能源数据场景中显示出实际适用性和卓越性能.
相关概念视频
01:20Downsampling
126
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
126
01:06Sampling Methods: Overview
269
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling.
In analytical chemistry, the choice of...
In analytical chemistry, the choice of...
269
01:16Stratified Sampling Method
11.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
To choose a stratified sample, divide the population into groups called strata and then take a...
11.7K
01:22Upsampling
194
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
194
01:11Sampling Continuous Time Signal
198
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
198
01:20Cluster Sampling Method
11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K