Long-memory time series ensembles for concept shift detection
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
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In this work we conduct an empirical study of opinion time series created from Twitter data regarding the 2008 U.S. elections. The focus of our proposal is to establish whether a time series is appropriate or not for generating a reliable predictive model. We analyze time series obtained from Twitter messages related to the 2008 U.S. elections using ARMA/ARIMA and GARCH models. The first models are used in order to assess the conditional mean of the process and the second ones to assess the conditional variance or volatility. The main argument we discuss is that opinion time series that exhibit volatility should not be used for long-term forecasting purposes. We present an in-depth analysis of the statistical properties of these time series. Our experiments show that these time series are not fit for predicting future opinion trends. Due to the fact that researchers have not provided enough evidence to support the alleged predictive power of opinion time series, we discuss how more rigorous validation of predictive models generated from time series could benefit the opinion mining field.