MC-ANN: A Mixture Clustering-Based Attention Neural Network for Time Series Forecasting

Time Series Forecasting (TSF) has been researched extensively, yet predicting time series with big variances and extreme events remains a challenging problem. Extreme events in reservoirs occur rarely but tend to cause huge problems, e.g., flooding entire towns or neighborhoods, which makes accurate reservoir water level prediction exceedingly important. In this work, we develop a novel extreme-adaptive forecasting approach to accommodate the big variance in hydrologic datasets. We model the time series data distribution as a mixture of both point-wise and segment-wise Gaussian distributions. In particular, we develop a novel End-To-End Mixture Clustering Attention Neural Network (MC-ANN) model for univariate time series forecasting, which we show is able to predict future reservoir water levels effectively. MC-ANN consists of two modules: 1) a grouped Auto-Encoder-based Forecaster (AEF) and 2) a mixture clustering-based learnable Weights Attention Network (WAN) with an attention mechanism. The WAN component is crucial, skillfully adjusting weights to distinguish data with varying distributions, enabling each AEF to concentrate on clusters of data with similar characteristics. Through extensive experiments on real-world datasets, we show MC-ANN's effectiveness (10-45% root mean square error reductions over state-of-the-art methods), underlining its notable potential for practical applications in univariate, skewed, long-term time series prediction tasks.