Time series is a collection of data instances that are ordered according to a time stamp. Stock prices, temperature, etc are examples of time series data in real life. Time series data are used for forecasting sales, predicting trends. Visualization is the process of visually representing data or the relationship between features of a data either in a two-dimensional plot or a three-dimensional plot. Visualizing the time series data constitutes an important part of the process for working with a time series dataset. Visualizing the data not only helps in the modelling process but it can also be used to identify trends and features that cause those trends. In this work, we take a real-life time series dataset and analyse how the target feature relates to other features of the dataset through visualization. From the work that has been carried out, we present an effective method of visualization for time series data which will be much useful for machine learning modelling with such datasets.
翻译:时间序列是按时间标记排列的数据实例的集合。 股票价格、温度等是真实生活中时间序列数据的实例。 时间序列数据用于预测销售量和预测趋势。 可视化是视觉代表数据的过程,或者是在二维图或三维图中数据特征之间的关系。 时间序列数据的可视化是使用时间序列数据集的过程的一个重要部分。 可视化数据不仅有助于模拟过程,还可以用来确定造成这些趋势的趋势和特征。 在这项工作中,我们使用一个真实时间序列数据集,分析目标特征如何通过可视化与数据集的其他特征相联系。我们从已经开展的工作中,为时间序列数据提供了一个有效的可视化方法,这将对用这些数据集进行机器学习模型非常有用。