The data we collected is very

complicated

, including a large amount of data points and multiple data sources.

This is mainly due to many changes that affect sales, and these factors will also affect each other. In order to get the most accurate value, we must comprehensively consider all factors. Therefore, there are many well -known algorithms in the field of big data computing, such as linear regression, which cannot be used to solve these problems after testing.

Therefore, we have developed a unique algorithm system that considers all relevant data points to obtain the accuracy that currently can achieve. Optimize the data model of each product category

Because each product category is different, each has its own characteristics, the data is different, and the relationship between data may also be over time with time with time. Change and change.

In ACCUSALES ™, we will update the data models of each product category every month to ensure the use of the optimal algorithm analysis of product data and market trends.

Filtering abnormal values \u200b\u200b

In addition, abnormal values \u200b\u200bare often encountered when processing a large amount of complicated data, such as the sales of certain products increased abnormally due to the sales of certain products. Therefore, we also consider these factors when designing the Accusales ™ algorithm system, so that it can filter out these abnormal values \u200b\u200bvery accurately and prevent the normal calculation of its interference data.

How often does it take to update it?

We are collecting, analyzing and comparing data every day. When the data deviation is found too large, the sales prediction value will be immediately corrected and the algorithm system is adjusted as needed.

When the data prediction is unstable, we will update the system every day. When the data is stable, it will be updated every week or every two weeks.