Network traffic data preparation and preprocessing
(2)
Decompose the raw data into several different modal components and obtain some subsequences of IMF1, IMF2, IMF3, …, IMFn
(3)
Divide each subsequence into a training set and a test set
(4)
Construct the LSTM network traffic prediction model. Set partial parameters and fix the number n of the optimized parameter
(5)
IPSO parameter initialization (particle swarm size m, solving space dimension d, the maximum number of iterations iter_max, learning factor ,, weight ω)
(6)
Initialize the values of n-dimensional parameter combinations of m groups randomly in the solution space
(7)
Initialize the global optimal parameter combination gbest_parameters, the partial optimal parameter combination pbest_parameters and the best fitness function value Pg
(8)
While the end condition is False
(9)
Apply the n-dimensional parameter combinations of m groups, respectively, to the LSTM network traffic prediction model for training, and calculate the current fitness function value;
(10)
Get the current best fitness value Pi and the corresponding parameter combination pbest_parameters;
(11)
if Pi ＜ Pg;
(12)
;//Update the best fitness value
(13)
gbest_parameters = pbest_parameters;//Update the global optimal parameter combination
(14)
end if;
(15)
for each parameter combination
(16)
Calculate the search direction and position of the new parameter combination according to equations (8) and (9)
(17)
Fix the updated parameter in the selected values;
(18)
end for;
(19)
The number of iterations + 1;
(20)
end while;
(21)
Return to gbest_parameters;
(22)
Introduce gbest_parameters into the LSTM network traffic prediction model;
(23)
Predict test data of each subsequence and gain results1,result2, result3,…, resultsn;
(24)
Superpose the subsequence prediction results of results1, result2, result3,…, resultsn and output the network traffic prediction result.
