Time-series models have vast advantages in the study of dynamic systems, especially if the aims are to determine structure and stability of population or finding regime shifts in dynamic characters of ecological systems. These models can also be used with precise goals for extracting specific demographic functions and their impacts. Auto Regressive Integrated Moving-Average or ARIMA models are one of the most general class of time-series forecasting models. In the present study, eight different environmental factors were chosen as the target groups for studying time series variations, that ranged from physico-chemical (e.g. air and water temperature, humidity, etc.) to biological (zooplankton) factors. Most of the ARIMA models were able to capture the trends of variation in the observed data. However, some linear trends were also observed in few of the forecasted series (increasing for some and decreasing for others). To improve on these forecasts, a hybrid ARIMA-ANN method was utilized which successfully increased the accuracy of future predictions showing seasonal variations in the forecasted values.

时间序列模型在动态系统的研究中具有巨大的优势，尤其是当目标是确定人口的结构和稳定性或寻找生态系统动态特征的状态变化时。这些模型还可以与精确的目标一起使用，以提取特定的人口统计功能及其影响。自回归综合移动平均或ARIMA模型是最常规的时间序列预测模型之一。在本研究中，选择了八种不同的环境因素作为研究时间序列变化的目标组，其变化范围从物理化学因素（例如空气和水的温度，湿度等）到生物因素（浮游生物）。大多数ARIMA模型都能捕捉到观测数据的变化趋势。然而，在少数预测系列中也观察到一些线性趋势（某些趋势增加，另一些趋势减少）。为了改善这些预测，使用了一种混合ARIMA-ANN方法，该方法成功地提高了未来预测的准确性，这些预测显示了预测值的季节性变化。