| experiment name |
Sparse coefficient model and seasonal model |
| experiment content |
1. Simple season model |
| experiment purpose |
1. Master the sparse coefficient model 2. Skilled in building seasonal models |
table of Contents
Simple seasonal model structure
Parameter estimation and model checking
Recommended reading
- Use Python to complete the basics of time series analysis
- A practical case of SPSS establishing a time series multiplication season model
- Practical case of building a time series ARIMA model in Python
Simple seasonal model structure
Model building
Timing diagram
The time series diagram shows that the series contains both long-term trends and seasonal effects with annual cycles
Difference smoothing
Do 1st-order difference to eliminate the trend of the original sequence, and then do 4-step difference to eliminate the influence of seasonal effects. The sequence diagram of the sequence after the difference is:
Unit root test:
White noise test
The test results show that the differential sequence is a stationary non-white noise sequence, and the differential sequence needs to be further fitted to the ARMA model.
Model ordering
The autocorrelation graph shows a clear downward trajectory, which is a typical trailing attribute. The partial autocorrelation plot except for the 1st and 4th order partial autocorrelation coefficients is significantly larger than 2 times the standard deviation. So try to fit ARIMA(4,1,0)*(0,1,0)4
Parameter estimation and model checking
x2, x3, P>α, fail the significance test
Significance test of the model:
The test results show that the residual sequence is a white noise sequence, and the parameter significance test shows that both parameters are significantly non-zero.
Model prediction
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