Prediction of Interbank Offered Rate Based on Time Series Model

Ding Huang,Ming Zhong,Xupeng Shi,K.H.M Mansur,Y Fu

doi:10.1051/e3sconf/202125101014

Ding Huang, Ming Zhong + Show 3 more

Open Access

https://doi.org/10.1051/e3sconf/202125101014

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Abstract

This paper studies the prediction of interbank offered rate changes in each working day. Using the actual data of each working day of China’s interbank offered rate from 2007 to 2019, this paper sets up ARIMA, Prophet, grey model and MTGNN to study and verify the time series data, and make a comparison between these models. The limitation of this paper is that it does not consider the impact of macroeconomic characteristics but only considers the predict changes in time series. The results of this paper are expected to be helpful for bank management and interbank transaction decision making.

Highlights

Time series data is usually one series or multiple series of numerical data represented in a time dimension
Liu[8] et al in order to explore the multifrequency stock model, this paper proposes a model adaptive wavelet transform (AWTM), according to the dynamic changes of the input sequence automatically focus on different frequency component in order to solve the problem of the stock prediction
The above two types of data were input into ARIMA, grey model, Prophet and MTGNN respectively to get the prediction results

Summary

Introduction

Time series data is usually one series or multiple series of numerical data represented in a time dimension. It is a common activity to record the changes of these time series data, including a large number of time series data in the fields of weather, commerce, agriculture, finance and medical research. The prediction research for time series data covers many application fields, and plays an important role in these fields. The research on the interbank interest rate prediction can help the national economic market to understand the accurate real-time economic trend, and provide the basic reference for the financial institutions such as banks to regulate the market and the trend of the economic situation[1]. The second chapter introduces the related work of time series data prediction. The fifth chapter summarizes the research work and conclusion of this paper

Related work

Prophet

Prediction results and discussion transmission are as follows

Model training

Conclusion