Machine learning predicts long-term bet365 mobile betting better than traditional models
September 25, 2024 10:22 Source: "Chinese bet365 mobile betting" Issue 2983, September 25, 2024 Author: Yao Xiaodan/Compiler

  bet365 mobile betting newspaper comprehensively reports from foreign media bet365 mobile betting is an important indicator of economic conditions,Whether it is possible to accurately predict bet365 mobile betting levels in various places has a negative impact on local governments、Businesses, families and individuals are all important。Many countries implement bet365 mobile betting targeting,This means they will set a specific bet365 mobile betting target,And utilize a range of policy tools to achieve relevant goals。Therefore,If you cannot correctly predict the future development trend of bet365 mobile betting,May have a negative impact on the economy of a country or region。

The official website of the Russian National Research Higher Economics University recently issued an article stating,Researchers at the university discovered,Machine learning techniques outperform traditional econometric models in long-term bet365 mobile betting forecasts。Proposed by researchers,There are a variety of data that can be used to predict bet365 mobile betting,includes consumer price index、Unemployment rate、Exchange rate and base interest rate set by the central bank。To examine which model can more accurately predict bet365 mobile betting in a certain region,Is it a traditional econometric model or a newer machine learning technology,They conducted the test using data from some regions in Russia between January 2010 and December 2022。

Researchers believe,To use machine learning technology to make predictions and ensure accuracy,Selecting the best hyperparameters for your bet365 mobile betting is crucial。When using machine learning technology,Hyperparameters are parameter data set before starting the learning process,Instead of parameter data obtained through training。To ensure the stability and accuracy of predictions,The researchers adopted the method of cross-validation using test samples of the same size。This method allows using data from one period to train the bet365 mobile betting,Test using data from another period。

Research results show,Comparison with other machine learning models used to predict regional bet365 mobile betting,The prediction results of the gradient boosting model are the most accurate。Compared with the traditional autoregressive model,Gradient boosting models can provide more accurate predictions over longer time horizons。at 3、6、over 21 and 24 month forecast periods,The prediction results of the gradient boosting model are better than those of the autoregressive model。Autoregressive model is a method of processing time series that can be used for statistics,It assumes that the current value of a sequence depends on its previous value plus random error。On 21-month and 24-month long-term forecasts,Random forest models and support vector machines also performed well。

In the opinion of researchers,Their research results show,Using machine learning technology to effectively predict bet365 mobile betting in different time ranges,And compared with traditional econometric models,Machine learning provides more reliable tools for long-term forecasting。However,Traditional econometric models still play a key role in short-term forecasts,Should not be completely excluded from the analyst’s toolkit。Combining traditional econometric models with machine learning techniques,Can significantly improve regional bet365 mobile betting forecast accuracy。In an environment of highly uncertain and rapidly changing economic conditions,This is particularly important。

Researchers also suggested,bet365 mobile betting forecasts in different regions may have different specific characteristics,Each region has its own economic structure、Characteristics related to natural resources and geographical location,These factors can lead to changes in bet365 mobile betting dynamics and key macroeconomic indicators。(Yao Xiaodan/Compiled)

Editor: Zhang Jing
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