A multivariate real-time time sequence data prediction method and system

Abstract

A multivariate real-time time sequence data prediction method comprises the steps of obtaining historical data of structured digital data and unstructured text data, and storing the historical data into a database; Reading historical data in a database, and transferring the historical data into time sequence data in the form of time content key value pairs; Converting the time sequence data into asample data set; Constructing a hot spot integration prediction model, and training the model by using the sample data set; And loading the trained model, processing the real-time data flow by usingthe model to obtain an inference result, and outputting the result. By means of the method, the problems that long-term influence of a traditional model on the exchange rate is insufficient in representation force and low in fitting precision, and existing foreign exchange prediction is limited to technical face analysis on exchange rate data and does not consider the influence of a macroeconomicbasic face can be solved.

Description

technical field [0001] This field relates to the computer field, and more specifically relates to a method and system for multivariate real-time time series data prediction. Background technique [0002] With the legalization of the global floating exchange rate system and the strengthening of the trend of world economic integration, foreign exchange, as an important capital element, has become an important component of many capital products, so its prediction has attracted the attention of many scholars and investors. However, due to the sensitivity of foreign exchange capital to the market and its own completeness, it is still impossible to directly and effectively predict the exchange rate data. [0003] For quantitative economy, the analysis and forecast of the foreign exchange market is usually based on the historical data of the exchange rate, and the corresponding data analysis method is used to construct the forecast model. However, due to the influence of many exte...

AI Technical Summary

Problems solved by technology

[0009] In view of this, the purpose of the embodiments of the present invention is to propose a method and system for multivariate real-time time series data prediction, which can solve the problem of insufficient representation of the long-term impact of traditional models on exchange rates, low fitting accuracy, and the current foreign exchange forecast is limited to exchange rate data The technical analysis does not consider the impact of macroeconomic fundamentals

Images