PRRE5008 - Use the Tennessee Eastman Data and fit a Random Forest Model - Report Writing - Engineering Assignment Help

Assignment Task

Write a report in which analyses of the following problems are described. This should include a concise summary of the algorithms that were used, the results of the analyses, as well as some conclusions regarding the quality of the solutions. You are free to use any suitable software of your choice.

-Use the Tennessee Eastman (tedata.txt) data and fit a random forest model to the data.
-Discuss optimization of the model in terms of the number of candidate variables and observations selected at each split when building the trees in the forest, as well as the number of trees in the forest.
-Use the variable permutation or the Gini approach associated with random forests to rank the variables in order of importance in the model.
-If the contributions of any variables are statistically insignificant, indicate these and rerun the model after elimination of these variables from the data (the optimal hyperparameters determined in (a) can be used again – no need for reoptimisation.
-Use a multilayer perceptron instead of the random forest model.
-Discuss optimization of the model in terms of the number of the number of nodes used in the hidden layer or layers (if you use more than one).
-Use an approach of your choice with the multilayer perceptron to rank the variables in order of importance in the model. As before, if the contributions of any variables are statistically insignificant, indicate these and rerun the model after elimination of these variables from the data (the optimal hyperparameters determined in (a) can be used again – no need for reoptimisation. Do both models yield the same results?
Generate a time series (ft) as follows:
ft=0.001(t-100)2+2sin2?tp1+0.75sin2?tp2+N(-1,1)In this time series, t={0,1,…N-1}, N=200, p1=20 and p2=30. N(-1,1) is a uniform random number between -1 and 1.

-Make use of a principal component model to decompose the time series into three components, associated with the 1st, 2nd and 3rd combined and all the remaining components combined. Plot these components.

Hint: A summary of the theory (and a Python version) of the approach can be found at https://www.kaggle.com/jdarcy/introducing-ssa-for-time-series-decompositionMake use of an autoencoder model to decompose the time series into three components, equivalent to the ones used in 3(a). Plot these components and discuss the reliability of the models in 3(a) and 3(b).

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