Advances of IoT (Internet of Thing) generate a sheer volume of floating-point time series data, which poses great challenges in storing and transmitting these data. To this end, floating-point time series data compression is extremely crucial. It can be classified into lossy and lossless compression based on data reversibility. Lossy compression methods achieve a better compression ratio by discarding some data information and are suitable for applications with lower precision requirements. Lossless compression methods, while reducing data size, retain all data information, which is essential for applications that require maintaining data integrity and accuracy. In addition, to meet the requirements of real-time monitoring on edge devices, streaming compression algorithms emerge. Current review studies on time series compression encounter issues such as incomplete sorting, unclear line of thought, single classification standards, and lack of inclusion of relatively new and representative algorithms. Time series compression algorithms over the years are divided into lossy compression and lossless compression. Then, different algorithm frameworks are further distinguished, including those based on data representation, prediction, machine learning, and transformation. Meanwhile, the compression characteristics of streaming and batch processing are summarized. Then, the design ideas of various compression algorithms are deeply analyzed, and the development context diagrams of these algorithms are presented. Next, the advantages and disadvantages of various algorithms are compared with experiments. Finally, common application scenarios are summarized. Future research is envisioned.