论文标题
使用统计和偏差接近无损的时间序列数据压缩方法
Near Lossless Time Series Data Compression Methods using Statistics and Deviation
论文作者
论文摘要
在过去的二十年中，由于最近的技术（包括物联网（IoT），E-Health，E-Health，Industrial Iot 4.0，自动驾驶汽车等）的实现，数据收集的增长巨大。数据传输和存储的挑战可以通过使用最新的最新数据压缩方法来处理数据传输和存储的挑战。最新的数据压缩方法是使用深度学习方法提出的，该方法的性能比常规方法更好。但是，这些方法需要大量的数据和资源进行培训。此外，由于物联网设备的资源约束性质，很难在物联网设备上实现这些基于学习的解决方案。在本文中，我们提出了基于数据统计和偏差的轻质数据压缩方法。就压缩比（CR）而言，所提出的方法的性能优于深度学习方法。我们模拟和比较了各种时间序列信号的提议的数据压缩方法，例如加速度计，气体传感器，陀螺仪，电力消耗等。特别是，观察到，所提出的方法可实现250.8 \％，94.3 \％，94.3 \％和205 \％的CR比gys gys，gactive，gactive and acm和acm高。代码和数据可从https://github.com/vidhi0206/data-compression获得。
The last two decades have seen tremendous growth in data collections because of the realization of recent technologies, including the internet of things (IoT), E-Health, industrial IoT 4.0, autonomous vehicles, etc. The challenge of data transmission and storage can be handled by utilizing state-of-the-art data compression methods. Recent data compression methods are proposed using deep learning methods, which perform better than conventional methods. However, these methods require a lot of data and resources for training. Furthermore, it is difficult to materialize these deep learning-based solutions on IoT devices due to the resource-constrained nature of IoT devices. In this paper, we propose lightweight data compression methods based on data statistics and deviation. The proposed method performs better than the deep learning method in terms of compression ratio (CR). We simulate and compare the proposed data compression methods for various time series signals, e.g., accelerometer, gas sensor, gyroscope, electrical power consumption, etc. In particular, it is observed that the proposed method achieves 250.8\%, 94.3\%, and 205\% higher CR than the deep learning method for the GYS, Gactive, and ACM datasets, respectively. The code and data are available at https://github.com/vidhi0206/data-compression .
