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Inzata’s Automatic SARIMA Modeling For Accurate Forecasting

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Nikol H
A passionate tech enthusiast and seasoned tech blogger, Nikol's writing style is characterized by its clarity and accessibility. Whether demystifying the intricacies of artificial intelligence, or guiding readers through the world of data modeling, her articles are a beacon for those navigating the ever-evolving tech landscape.

SARIMA, or Seasonal Autoregressive Integrated Moving Average, is a powerful statistical model that is commonly used for time series forecasting. It is particularly useful for analyzing and predicting patterns in data that exhibit seasonality and trends, such as monthly sales figures or annual weather patterns. The model is able to take into account both the past and current values of the data, as well as any underlying seasonality or trends, to make accurate predictions about future values.

One of the key benefits of SARIMA models is their ability to provide valuable insights into the future development of an analyzed variable. This makes them extremely useful for businesses and organizations that need to make decisions based on projected future trends.

SArima modeling module in inzata

Inzata, a company that offers machine learning and artificial intelligence solutions, has developed an automatic SARIMA modeling module. This module is fully integrated into the data mining and machine learning processes of Inzata’s ML/AI module, making it easy to use and accessible to a wide range of users. The module is designed to be fully automatic, meaning that it can automatically handle all of the steps involved in creating a SARIMA model, from data preparation to model selection and validation.

One of the main goals of the Inzata SARIMA algorithm is to achieve high levels of predictive accuracy. In many cases, the algorithm is able to produce better forecasting results than hand-made models created by scientists, making it a valuable tool for businesses and organizations that rely on accurate forecasting to make strategic decisions.

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