Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction

This paper introduces an adaptive interval fuzzy modeling method using participatory learning and interval-valued stream data. The model is a collection of fuzzy functional rules in which the rule base structure and the parameters of the rules evolve simultaneously as data are input. The evolving nature of the method allows continuous model adaptation using the stream interval input data. The method employs participatory learning to cluster the interval input data recursively, constructs a fuzzy rule for each cluster, uses the weighted recursive least squares to update the parameters of the rule consequent intervals, and returns an interval-valued output. The method is evaluated using actual data to model and forecast the daily lowest and highest prices of the four most traded cryptocurrencies, BitCoin, Ethereum, XRP, and LiteCoin. The performance of the adaptive interval fuzzy modeling is compared with the adaptive neuro-fuzzy inference system, long short-term memory neural network, autoregressive integrated moving average, exponential smoothing state model, and the naive random walk methods. Results show that the suggested interval fuzzy model outperforms all these methods in predicting prices in the digital coin market, especially when considering directional accuracy measure.