A large part of any design of a data learning agent is in feature extraction of the underlying data, and how it is computed and represented. The best processes for extracting features for learning information from data typically take advantage of expert knowledge of the underlying data to either expose the most relevant features, reduced noise, and extract the most amount of independent information in the data. For many types of datasets, this might be challenging due to factors such as incoherence, abstractedness, or the sheer amount of noise present in the data. In designing features for Tsetlin machines, one is tasked to booleanize (or binarize) the underlying data, and under the presence of noise, this can be challenging. Furthermore, for notoriously complex high-dimensional data like noisy sequences, graphs, images, signal spectra, and natural language, creating encodings that are also interpretable for human reasoning in any post-hoc process can be difficult due to creating logic AND expressions that both take advantage of the relevant information in the data, but also lead to accurate expressions that can compete with other machine learning models. In this paper, we explore using Hyperdimensional Vector Computing (HV computing, or simply HVC) as an input layer to a novel Tsetlin machine architecture and apply it to learning, classifying, predicting, and generating sequences. Here, we argue that HVC can provide a robust layer of feature extraction due to the many computational advantages. This approach was first introduced in [1] and here, we streamline the approach to focus on sequences while further leveraging other attributes of HCV such as N-Gram sequence encoding and associative memory, while combining with TMs, to create a powerful hybrid methodology while remaining minimalist in memory sizes of the overall model.