Anomaly Detection for Large Time Series Data [closed]

Question

I am working on detecting anomalies within a large time series data set. It is updated on a regular basis and consists of more than 30 parameters. I am using R as a reference language.

It is a first for me working on this type of projects and I am unfamiliar with most of the techniques. I have 6 weeks to implement a good analytical toolbox to enhance the quality of the control checks on the production line.

I have found a couple of potential methods to analyze it including statistical machine learning, deep learning using auto-encoded neural networks or clustering approaches. The purpose of the chosen method is to detect the anomalies/outliers by itself. It doesn't really need to be real-time analysis. What approach would you recommend to implement for the scope of the project, given the structure of the data?