Forex Analysis Method- Autoregression

The premise behind autoregressive methods is that previous values in the time series directly influence the current value in the time series. Mathematically, this can be expressed as

Yx +1 = AYx + BYx −1 + CYx −2 + ε

where,

x the time increment

Y(x) the price at time index x

A the first regression coefficient

B the second regression coefficient

C the third regression coefficient

ε the error factor, whose sum approximates zero

This equation infers that the time-series closing price on any given day is the sum of the closing prices on the three previous days, all adjusted by regression coefficients. The number of inde- pendent variables on the right side of the equation determines the autoregressive order of the model.

Autoregression has numerous supporters in the realm of techni- cal analysis. It also has several variations and enhancements, such as the autoregressive integrated moving-average (ARIMA) time-series model introduced by George Box and Gwilym Jenkins in the early 1970s. This model frequently is designated as the ARIMA( p, d, q ) model, where p is the autoregressive order, d is differencing order, and q is the moving-average order.