Seasonality is a recurring pattern that occurs in data over a given time interval, which can be daily, weekly, monthly or yearly. Seasonality can be observed in many types of data, such as product sales, website traffic, crop production, among others.
In data analytics and machine learning, it is important to take seasonality into account, as it can affect the accuracy of models and predictions. Models that do not take seasonality into account can produce inaccurate or biased results. Therefore, it is important to identify seasonality in the data and adjust models to account for these recurring patterns.
Machine learning algorithms can help identify seasonality in the data and adjust models to account for these recurring patterns. For example, regression models can include seasonal variables to capture the effects of recurring patterns in the data. In addition, time series models can use specific techniques to model seasonality in the data and make accurate predictions.
The term Business Intelligence (or BI) defines the use of information technologies to identify, discover, and analyze business data, such as business [...]
Read More »Once the basic concepts for building a commercial software with artificial intelligence are clear, where it is defined to whom to dedicate effort and [...]
Read More »Artificial Intelligence (AI) derives from a series of models or branches that can be used in different areas of people's lives, as well as in different areas of [...]
Read More »Clustering methods, or grouping, are a fundamental part of the data analysis process, since they allow an automatic segmentation of the data [...]
Read More »
