I have a time series dataset with 63 features and a univariate dependent variable. This is my first major time series project, so I was wondering if algorithms like ARIMA and LSTM are immune towards variables that have collinearity, similar to gradient boosted methods such as xgboost that is actually seen as a robust method. Below is the image of the correlation matrix. I have used xgboost with the dataset and have not got satisfactory results, even after removing some of the variables that are in the red category, so I'll proceed with the next step by using some time series methods.
The image can always be zoomed to get a clearer description of features.