Detailed Market Description And Forecasting Based On Big Data Analysis

After years of informatization construction,the business running efficiency has been greatly improved in various enterprises.What`s more,there has been a huge amount of data accumulated in their servers during the running processes.In recent years,with the increasing awareness and the extraordinary development of big data technologies,information technology companies have begun to use data mining and analysis techniques to acknowledge from historical data.The rapid solution making relying on big data analysis benefits not only business strategy adjustments,but also speeding up the industry development.In the process of market analysis,one of the most concerned goals for companies is data-driven sales forecast analysis.Accurate sales forecasts can provide the most direct evaluation basis for companies when generating business solutions aiming at revenue growth.Sales forecasting has extraordinary guiding significance for business solution making.The development process of sales forecasting technology is tortuous,and the traditional forecasting path cannot meet the forecasting needs out of the complex business environment today.Whether it is forecasting technology or the collection and processing of source data.It is still an issue waiting to be solved in the field of sales forecasting no matter dealing with the raw data rapidly or getting an accurate sales forecast result.Meanwhile,it is still important to clarify these complex and multi-source business data to the business solution makers which indicates the great importance for us to develop the business data visualization applications.Above all three issues,we firstly did a business analysis based on our industry knowledge,and secondly conducted a series of sales forecasting modeling training experiments and built a visualization platform for describing the market situation at last.The main contribution points are as follows:(1)With reference to the game theory,we add some relevant features like the market share of all products to the dataset so that the sales forecasting model takes the internal and external characteristics of the enterprise into account which can lead to the improving of the accuracy and robustness of our forecasting model.And we gives a specific introduction to this part of the work in Chapter 3.(2)On the basis of time series analysis,the gradient boosting tree is used to further fit the model residuals and improve the combined model's performance to extract the regulation from data samples and find out the relationship among features.Chapter 5shows the designing ideas and experimental results of the combined model.(3)Build a visual analysis platform based on multi-source business data,which can exhaustively describe the performance of the current sales market in various dimensions,and provide sales forecasting result supporting further guidance in business solutions.And the specific content is in the Chapter 5.