Examples of techniques for determining an interval duration and a training period length for log anomaly detection are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include: determining, by a processing resource, an interval duration for a time series from a plurality of message IDs; and determining, by the processing resource, a training period length based on the interval duration.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for determining an interval duration and a training period length for log anomaly detection, the method comprising: determining, by a processing resource, an interval duration for a time series from a plurality of message IDs; and determining, by the processing resource, a training period length based on the interval duration. 2. The computer-implemented method of claim 1, wherein determining the interval duration further comprises: counting a number of messages per second; and writing the number of messages to a file. 3. The computer-implemented method of claim 2, wherein determining the interval duration further comprises: calculating, by the processing resource, a suggested interval duration based on the number of messages per second written to the file. 4. The computer-implemented method of claim 3, wherein determining the interval duration further comprises: determining whether the suggested interval duration provides a timely answer to a business problem. 5. The computer-implemented method of claim 4, wherein determining the interval duration further comprises: responsive to determining that the suggested interval duration provides a timely answer to a business problem, plotting a histogram of the plurality of messages; and determining whether the histogram of the plurality of messages using the interval duration selected exhibits a central tendency. 6. The computer-implemented method of claim 1, wherein determining the training period length further comprises: calculating a maximum amount of lag to allow. 7. The computer-implemented method of claim 6, wherein determining the training period length further comprises: identifying potential patterns in the time series using a partial auto correlation. 8. The computer-implemented method of claim 7, wherein determining the training period length further comprises: selecting a largest repeating pattern of a plurality of potential patterns. 9. The computer-implemented method of claim 8, wherein determining the training period length further comprises: determining whether an amount of non-stationarity exists. 10. The computer-implemented method of claim 9, wherein determining the training period length further comprises: responsive to determining that an amount of non-stationarity exists, displaying an auto correlation of the plurality of messages between the plurality of potential patterns. 11. A system for determining an interval duration and a training period length for log anomaly detection, the system comprising: a memory having computer readable instructions; and a processing device for executing the computer readable instructions, the computer readable instructions comprising: determining an interval duration for a time series from a plurality of message IDs; and determining a training period length based on the interval duration. 12. The system of claim 11, wherein determining the interval duration further comprises: counting a number of messages per second; and writing the number of messages to a file. 13. The system of claim 12, wherein determining the interval duration further comprises: calculating, by the processing resource, a suggested interval duration based on the number of messages per second written to the file. 14. The system of claim 13, wherein determining the interval duration further comprises: determining whether the suggested interval duration provides a timely answer to a business problem. 15. The system of claim 14, wherein determining the interval duration further comprises: responsive to determining that the suggested interval duration provides a timely answer to a business problem, plotting a histogram of the plurality of messages; and determining whether the histogram of the plurality of messages using the interval duration selected exhibits a central tendency. 16. The system of claim 11, wherein determining the training period length further comprises: calculating a maximum amount of lag to allow. 17. The system of claim 16, wherein determining the training period length further comprises: identifying potential patterns in the time series using a partial auto correlation. 18. The system of claim 17, wherein determining the training period length further comprises: selecting a largest repeating pattern of a plurality of potential patterns. 19. The system of claim 18, wherein determining the training period length further comprises: determining whether an amount of non-stationarity exists; and responsive to determining that an amount of non-stationarity exists, displaying an auto correlation of the plurality of messages between the plurality of potential patterns. 20. A computer program product for determining an interval duration and a training period length for log anomaly detection, the computer program product comprising: a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing device to cause the processing device to: determine an interval duration for a time series from a plurality of message IDs; and determine a training period length based on the interval duration.