The intradecadal variations in length-of-day (LOD) and their time-varying characteristics still need to be further studied. Given that the corresponding signal periods on the intradecadal scales are quite close to each other and the span of currently observed ΔLOD data (i.e., 1962–2019, only 57 years) is not long enough, accurate detection of these signals depends on using effective mathematical method. On the basis of the traditional harmonic retrieval model, this work proposes a frequency domain stepwise regression method, which can well identify the periodic components with close periods and recognize the weaker signals to estimate the relevant harmonic parameters (i.e., amplitude, frequency and phase). Furthermore, we apply this method to detection of the actual LOD intradecadal variations, the result of which shows that there are three components (i.e., the ~ 5.9 years, ~ 8.3 years and ~ 7.3 years) existing in LOD intradecadal variations. Here, we firstly give the time-domain harmonic expression of the ~ 7.3 years signal, i.e., \(y\left(t\right)=A\mathrm{cos}\left(\frac{2\pi }{T}\left(t-1962+\psi \right)\right)\), where the period \(T=7.33\) years, amplitude \(A=0.0413(\pm 0.0099)\) ms, phase \(\psi =-1.58(\pm 0.30)\) years. The mechanism of this ~ 7.3 years signal needs to be further studied.

日间交易量（LOD）的行业差异及其时变特性仍需要进一步研究。鉴于两会制尺度上的相应信号周期彼此之间非常接近，并且当前观测到的ΔLOD数据的跨度（即1962–2019，只有57年）不够长，因此，准确检测这些信号取决于使用有效的数学方法方法。在传统谐波检索模型的基础上，本文提出了一种频域逐步回归方法，该方法可以很好地识别具有近似周期的周期分量，并识别较弱的信号，以估计相关的谐波参数（即幅度，频率和相位）。 。此外，我们将这种方法应用于检测行业内实际LOD的变化，结果表明，LOD的行业差异存在三个组成部分（〜5.9年，〜8.3年和〜7.3年）。在这里，我们首先给出〜7.3年信号的时域谐波表达，即\（y \ left（t \ right）= A \ mathrm {cos} \ left（\ frac {2 \ pi} {T} \ left（t-1962 + \ psi \ right）\ right）\），其中周期\（T = 7.33 \）年，振幅\（A = 0.0413（\ pm 0.0099）\） ms，相位\（\ psi = -1.58（\ pm 0.30）\）年。这〜7.3年的信号机制需要进一步研究。