The time series data is chaotic, non seasonal, non stationary and random in nature. It becomes quite challenging to discover the hidden patterns of time series data. In this paper the time series data is predicted with the help of a machine learning algorithm i.e. Elephant Herd Optimization (EHO). Three different types of time series data are used to testify the superiority of the proposed method namely stock market data, currency exchange data and absenteeism at work. The data are first subjected to feature selection methods namely ANOVA and Friedman test. The feature selection methods provide relevant set of features which is fed to the neural network trained with the method. The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization. The results prove supremacy of EHO over other methods.

时间序列数据本质上是混乱的，非季节性的，非平稳的和随机的。发现时间序列数据的隐藏模式变得非常具有挑战性。在本文中，借助机器学习算法（即大象群优化（EHO））来预测时间序列数据。三种不同类型的时间序列数据用于证明所提出方法的优越性，即股票市场数据，货币兑换数据和工作缺勤率。首先对数据进行特征选择方法，即ANOVA和Friedman测试。特征选择方法提供了相关的特征集，这些特征集被馈送到使用该方法训练的神经网络。将该方法与其他方法进行了比较，如局部线性径向基函数神经网络和粒子群算法。