Anomaly Detection for Large Time Series Data 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?
Following J.Tukey, you should plot, draw graphs, visualize, etc... until you have a solid pack of examples.

Then make Tukey' fences on each of the 30 parameters. Let $q_1$ and $q_3$ be the 1st and 3rd quartiles, $d=q_3-q_1$ the inter-quartile distance, and define as outlier any observation outside the interval $q_1-k\cdot d &lt; x &lt; q_3+k\cdot d$, where $k$ is a constant. Traditionally, $k=1.5$ indicates an outlier and $k=3$ indicates the data is far out. However, the real value of $k$ should be tested against your examples.

Then make a cluster analysis (with a k-nearest neighbor) and define as outlier any point isolated in one cluster. Again, use your example to test various values of $k$.