I am very new to Time Series Analysis (together with R). I have been practising with some very simple datasets to understand how to decompose the time series into Trend, Seasonal and Error components and then check whether the Error component is Gaussian or not. Till this point, everything is pretty much clear to me. However, after the decomposition, how do I fit the trend and seasonal component (using R)? I am confused on 'to what' do I fit my trend and seasonal component? I mean what should my predictors be? For the trend, I can use the time (specifically for the example below, the four quarters) perhaps? And as such, what does the fitting of seasonal component imply and how can it be achieved? Below is one of time series datasets that I used. This time series is called 'jj' and is present in the 'astsa' package. The frequency of this time series is quarterly.: > jj Qtr1 Qtr2 Qtr3 Qtr4 1960 0.710000 0.630000 0.850000 0.440000 1961 0.610000 0.690000 0.920000 0.550000 1962 0.720000 0.770000 0.920000 0.600000 1963 0.830000 0.800000 1.000000 0.770000 1964 0.920000 1.000000 1.240000 1.000000 1965 1.160000 1.300000 1.450000 1.250000 1966 1.260000 1.380000 1.860000 1.560000 1967 1.530000 1.590000 1.830000 1.860000 1968 1.530000 2.070000 2.340000 2.250000 1969 2.160000 2.430000 2.700000 2.250000 1970 2.790000 3.420000 3.690000 3.600000 1971 3.600000 4.320000 4.320000 4.050000 1972 4.860000 5.040000 5.040000 4.410000 1973 5.580000 5.850000 6.570000 5.310000 1974 6.030000 6.390000 6.930000 5.850000 1975 6.930000 7.740000 7.830000 6.120000 1976 7.740000 8.910000 8.280000 6.840000 1977 9.540000 10.260000 9.540000 8.729999 1978 11.880000 12.060000 12.150000 8.910000 1979 14.040000 12.960000 14.850000 9.990000 1980 16.200000 14.670000 16.020000 11.610000 > decomp_JJ=stl(log(jj),s.window = 4) > plot(decomp_JJ) [![STL Plot for JJ ][1]][1] Any help on this is much appreciated! [1]: https://i.sstatic.net/8zWES.png