## 1 Introduction  
With the popularization of digitalization, computers and data processing equipment have penetrated into all walks of life, and analog communication has almost been replaced by digital communication. People are faced with the rapid growth of mass information, and the increasing amount of data transmitted, processed and stored has caused great pressure on the transmission bandwidth, storage capacity and processing speed. For example, the Fuji Electric's PowerSataliteII measurement terminal recorded a power quality fault of 2 s in length, and the generated recording file was 948 kB. It can be seen from this that the huge amount of data takes up a lot of limited storage space and network resources. Therefore, researching efficient data compression methods that reduce the redundancy existing in massive data has become an urgent need in many modern industries. This includes power quality signal compression in the power industry and ECG signal compression in the medical industry, which are periodic signals or quasi-periodic signals. Data compression is the smallest digital representation of the signal sent by the source, reducing the signal space that contains a given message set or data sampling set. The general steps of data compression and decompression are shown in Figure 1. The compression process mainly includes three steps: transform coding [1; 2; 3], quantization, and entropy coding [4; 5; 6; 7] or dictionary compression [8; 9]. The decompression process mainly includes three steps: entropy decoding or dictionary decompression, inverse quantization, and inverse transform. [PERSON], Q proposed a data compression method for power quality monitoring based on two-dimensional discrete cosine transform (DCT), which converts one-dimensional data into two-dimensional data for data compression [10]. [PERSON], R proposed a new three-phase power quality data compression method based on wavelet transform, which, combined with Lempel-Ziv-Welch (LZW) coding, achieves a good compression effect on power quality signal compression [11]. [PERSON], T proposed a multi-channel lossless ECG compression algorithm that uses exponentially weighted multi-channel linear prediction and adaptive Golomb-Rice coding [12]. [PERSON], S proposed a DPCM-based threshold data compression technology for real-time remote monitoring applications, which uses simple calculation and a high compression ratio [13]. [PERSON], [PERSON] demonstrated a unified GAN based signal compression framework for image and speech signals [14]. [PERSON], [PERSON] proposed a framework of combining machine learning based classification and pre-existing coding algorithms, along with a novel revamp of using DPCM for DWT coefficients [15]. In order to improve the accuracy of prediction, [PERSON], [PERSON] proposed a novel ECG signal prediction method based on the autoregressive integrated moving average (ARIMA) model and discrete wavelet transform (DWT) [16]. [PERSON] proposed a hybrid spatio-temporal model by combining the generalized space-time autoregressive with exogenous variable and recurrent neural network (GSTARX-RNN) for space-time data forecasting with calendar variation effect [17]. The pattern matching predictor has been studied for a long time with many results. [PERSON], [PERSON] proposed a universal predictor based on pattern matching, called the sampled pattern matching (SPM), which is a modification of the Ehrenfeucht-Mycielski pseudorandom generator algorithm [18]. Although SPM has a good prediction ability for periodic signals, it involves pattern matching. The complexity of the algorithm is increased by the matching of the maximal suffix, which makes the real-time performance of the algorithm worse. [PERSON] proposed the FS predictor, which can calculate the minimum fraction of prediction errors [19]. The FS predictor mainly predicts binary sequences. For multi-bit wide digital sequences, its complexity is high.