Today, we’re focusing to detect anomaly within the economic indicator’s dataset. Anomaly detection is a powerful statistical technique used to identify unusual patterns that do not conform to expected behavior. These outliers can often provide critical insights.
In Essence, think of anomaly detection as the process of finding the needles in the haystack. In the context of our economic data, these ‘needles’ could be unusual spikes or dips in indicators like unemployment rates or hotel occupancy. Identifying these anomalies is crucial because they could signal significant economic events, shifts, or even errors in data collection.
We’re trying to employ the Isolation Forest method, a sophisticated algorithm well-suited for pinpointing anomalies in complex datasets. This technique is especially effective in handling large, multidimensional data, making it ideal for our purpose.