The indicator-based approach to fish stock assessment uses many indicators that characterize different attributes of a fish stock in order to assess its status. We considered 19 biological indicators to characterize location, dispersion, traits, fishing and abundance for the small yellow croaker, the indicators were derived from the 16-years (2000-2015) series of bottom trawl surveys over the East China Sea. The one-lag variogram for each indicator was computed, scaled to the indicator variance and ranked, the indicators with highest continuity at lag-one were selected. Min/max autocorrelation factors (MAFs) were calculated for the period 2000-2015 to summarize the multiple time series, detect changes and identity which indicators were responsible for the detected change. According to the variogram results, seven of the 19 indicators exhibited a marked time correlation at the first lag of the variogram below one, including four biological traits (sex ratio, allometric growth coefficient, condition factor, and third quartile of fish length), two spatial indicators (gravity in latitude and spreading area), and one abundance indicator (biomass index). Then the seven selected indicators were used to calculate MAFs during 2000-2015. The first two MAFs had low one-lag variogram values, 0.16 and 0.19, respectively, which represented lower time continuity. The continuity index was also calculated for each of the seven indicators on the first two MAFs, and the four indicators (YCG, BS, SexratioS, and SA) with the highest continuity index were selected to represent the history of the stock. The observed trends of the multivariate time series are described through the MAFs scores. MAF1 divided 16 years into three regimes (2000-2002, 2003-2012, and 2013-2015), and two trends were observed. MAF1 increased from 2000 until 2007 and then decreased until 2015. Whereas MAF2, which was not monotonic and had very small discontinuities, was detailed in two regimes (2000-2012 and 2010-2014) with four trends. From 2000 to 2003, it was close to being flat. From 2003 to 2006, it decreased, and it increased from 2006 to 2012, after which it decreased until 2015. The indicators that contributed the most to MAF1 were YCG (-0.756) and BS (-0.609), and the indicators that contributed the most to MAF2 were SexratioS (0.590), BS (0.539), and SA (-0.606). MAF1 was negatively correlated to YCG and BS, whereas MAF2 was positively correlated to SexratioS and BS but negatively correlated to SA. Due to the different inter-annual variation of biological traits and spatial indicators, the MAFs also exhibited different temporal change patterns at the different time scales.