A study on Trend Classification using Hurst Exponent for Time Series Data of Air Dose Rate

Since the Fukushima Daiichi nuclear power plant accident in 2011, interest in radiation has increased. Recently, air dose rate has been measured extensively by individuals and local governments. Although affordable instruments have become available, they lack continuous, long-term data recording, making it difficult to track changes in everyday environments. By analyzing data characteristics, early anomaly detection may be possible. Therefore, this research develops an air dose rate recording system that transfers measurement data to a computer by periodically capturing the instrument display via image recognition. We analyze the temporal trends by calculating the Hurst exponent of data obtained from this system.