A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting

Abstract

Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.

A non-parametric softmax for improving neural attention in time-series forecasting

“…The model is evaluated using two datasets from [26], which show that the model can converge faster to a higher level of accuracy. We also compare its performance on a third dataset that we collect specifically for this paper, on the short-term forecasting of Bitcoin closing prices, a task of significant practical interest lately, e.g., [17,2,3].…”

“…While both problems could be solved by introducing appropriate constraints on the α i coefficients, a simpler solution is to innovatively combine KAFs and the softmax as follows: where λ i are the coefficients in (3). Like in a standard softmax, exponentiation guarantees the positivity of the outputs, while the denominator ensures that the weights sum to one.…”