Is window based sequencing a good idea to obtain more training data for LSTMs?
Splitting a time-series into analysis windows, usually with overlap, is quite common practice. In anomaly detection, but also in classification or forecasting. It works great as long as your anomalies can be detected by analyzing such windows independently.
In such a setup, the length of the analysis window becomes a critical hyper-parameter - and will be data/task-dependent.
To get an overall anomaly score you can use any standard statistics to merge scores for individual windows. A simple mean would be the first thing to try, but there are more approaches.