Back to Time for Time SeriesTime for Time Series - 2 June 2022
Root Cause Analysis
Data analysis is performed to gain business insights from data. Before taking decisions based on these insights, it is important that the results of the analysis are well understood. A decision should be taken based on an unexpected forecast if that forecast reflects the historical situation, not if some input data accidentally went missing. A decision should be taken based on a true detected anomaly, not if some sensor passes faulty measurements.
TIM’s root cause analysis features allow users to dive deep in TIM’s results and pinpoint exactly where the root cause behind some prediction lies, and how to go looking for it.
Relevant links:
- TIM Forecast
- TIM Detect
Time for Time Series dives into specific features and looks at general functionalities in TIM, as well as covering many topics related to time-series analysis. We welcome everyone, from managers to data scientists, to get to know TIM and to ask your questions to our experts during the live Q&A.
