Data Science, Machine Learning, Unsupervised Learning.
φ₁Y(t-1) + φ₂Y(t-2) + … + φₚY(t-p) + ε(t) + θ₁ε(t-1) + θ₂ε(t-2) + … + θ_qε(t-q) Where: Y(t) represents the value of the time series at a time “t”. C is a constant term φ₁, φ₂, …, φₚ are the autoregressive parameters. ε(t) is the white noise term at a time “t”. θ₁, θ₂, …, θ_q are the moving average