Oriented to a multi-learning mode: Establishing trend-fuzzy-granule-based LSTM neural networks for time series forecasting

In the construction of information granule based neural networks for time series multi-step forecasting, the existing works tend to focus on consecutive-learning mode while rarely explore multi-learning mode. In fact, only under the multi-learning mode can diversified associations among data collected over time granules be well learned. Also, the existing works exhibit limited time interpretability. Here the problem centers around how to endow information granule based neural networks with a multi-learning mode to learn diversified associations simultaneously, and well-articulated trend semantics. To solve these problems, the first originality of this paper stems from a scale equalization method for multilinear-trend fuzzy information granules to track complex trend changes of data in a more accurate and explainable manner from both global and local views. Furthermore, an adaptive rather than empirical or traversal method, which is trend-driven in nature, is tailored for mining diversified associations. The resulting model can give forecasts in the form of granules as well as numerical values, being interpretable and accurate in the sense that: (a) its inputs and output are granules which come with well-defined trend semantics under a customary time concept, and (b) a clump of data is considered in a concise granule whilst roles of diversified associations are ware of during forecasting, making the model less prone to cumulative errors. Appealing experimental results corroborate the effectiveness of the proposed model.