A linear predictive analysis method for obtaining a coefficient which can be converted into a linear predictive coefficient corresponding to an input time series signal for each frame which is a predetermined time interval, the input time series signal being a digital audio signal, a digital acoustic signal, an electrocardiogram, an electroencephalogram, a magnetic encephalography or a seismic wave, the linear predictive analysis method comprising: an autocorrelation calculating step of calculating autocorrelation R o (i) between the input time series signal X o (n) of a current frame and the input time series signal X o (n-i) i sample before the input time series signal X o (n) or the input time series signal X o (n+i) i sample after the input time series signal X o (n) for each of at least i = 0, 1,..., P max; and a predictive coefficient calculating step of obtaining a coefficient which can be converted into linear predictive coefficients from the first-order to the P max - order using modified autocorrelation R' o (i) obtained by multiplying the autocorrelation R o (i) by a coefficient w o (i) for each corresponding i, characterized in that a case where, for at least part of each order i, the coefficient w o (i) corresponding to each order i monotonically increases as a period, a quantization value of the period, an estimate value of the period or a value having negative correlation with a fundamental frequency based on the input time series signal in the current frame or a past frame increases, and a case where the coefficient w o (i) monotonically decreases as a value having positive correlation with intensity of periodicity of the input time series signal in the current frame or the past frame or a pitch gain of the input time series signal which is the digital audio signal or the digital acoustic signal in the current frame or the past frame increases, are comprised, wherein the period is obtained by a periodicity analysis. an autocorrelation calculating step of calculating autocorrelation R o (i) between the input time series signal X o (n) of a current frame and the input time series signal X o (n-i) i sample before the input time series signal X o (n) or the input time series signal X o (n+i) i sample after the input time series signal X o (n) for each of at least i = 0, 1,..., P max; and a predictive coefficient calculating step of obtaining a coefficient which can be converted into linear predictive coefficients from the first-order to the P max - order using modified autocorrelation R' o (i) obtained by multiplying the autocorrelation R o (i) by a coefficient w o (i) for each corresponding i, characterized in that a case where, for at least part of each order i, the coefficient w o (i) corresponding to each order i monotonically increases as a period, a quantization value of the period, an estimate value of the period or a value having negative correlation with a fundamental frequency based on the input time series signal in the current frame or a past frame increases, and a case where the coefficient w o (i) monotonically decreases as a value having positive correlation with intensity of periodicity of the input time series signal in the current frame or the past frame or a pitch gain of the input time series signal which is the digital audio signal or the digital acoustic signal in the current frame or the past frame increases, are comprised, wherein the period is obtained by a periodicity analysis.