A time series long-short term codec for compression and representation

作者：Hong‐Xia Zuo, Junjie Wu, King Hann Lim, Yinping Liao, Luping Song, Yanping Zhu, Zhenjun Li, Chenhui Zhang · 发表于：Applied Intelligence · 年份：2025 · DOI：10.1007/s10489-025-06842-2 · 被引用次数：17 · 研究领域：Time Series Analysis and Forecasting、Advanced Data Compression Techniques、Chaos control and synchronization

Data compression is highly required to reduce the massive size of data while achieving lossless information over the transmission. In this paper, a novel multi-channel time series codec framework (LSCodec) is proposed to decouple long-term trend and short-term fluctuation using manually guided preprocessing data. The proposed LSCodec contains an encoder-decoder architecture network integrated with a group residual vector quantizer. The input data is decoupled through two paths layer by layer. In one path, a LSTM model is introduced for long-term trend feature learning. Another path produces fluctuation signal by subtracting between the real-time series and the trend signal to learn its hidden representation. The output of both path are then quantized using two individual residual vector quantization. A joint reconstruction loss combining its trend and fluctuation loss is used to support the training process. A balancer is used to stabilize training gradient of reconstruction loss to avoid local optimal solution or unstable state. Our experimental results show that the compression rate can vary to different bite rate according to strides setting. For multi-channel time series, it can compress data into average 10% with an acceptable reconstruction result. By reducing part of coding index, it is able to reconstruct part of curve with its main distribution. LSCodec can achieve a relatively good result in downstream task for a dataset that contains high ratio of anomaly. The prop...