AI RESEARCH
QARIMA: A Quantum Approach To Classical Time Series Analysis
arXiv CS.AI
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ArXi:2604.08277v2 Announce Type: replace-cross We present a quantum-inspired ARIMA methodology that integrates quantum-assisted lag discovery with fixed-configuration variational quantum circuits (VQCs) for parameter estimation and weak-lag refinement. Differencing and candidate lags are identified via swap-test-driven quantum autocorrelation (QACF) and quantum partial autocorrelation (QPACF), with a delayed-matrix construction that aligns quantum projections to time-domain regressors, followed by standard information-criterion parsimony.