Abstract: Addressing the nonlinear, fluctuating nature of system data and the insufficient sensitivity of existing grey accumulation to new information, an improved grey Bernoulli model with a damping trend factor (DANGBM(1,N)) is proposed. A damping accumulation operator is introduced, assigning greater weight to new data during preprocessing to enhance sensitivity to recent trends. Combined with the grey Bernoulli model, it better identifies nonlinear characteristics and flexibly adjusts prediction trends, overcoming limitations in existing techniques. Hyperparameters are optimized via particle swarm optimization for adaptability. Applied to China’s high-tech industry output value, the model demonstrates superior predictive performance over five benchmarks, offering an effective method for complex nonlinear small-sample forecasting.

Key words: Grey prediction, Damped accumulation operator, Grey Bernoulli model, Particle swarm optimization, High-tech industry