Application Research of Spline Interpolation and ARIMA in the Field of Stock Market Forecasting

TL;DR

This paper combines spline interpolation with ARIMA to improve short-term stock market forecasting accuracy, especially in the presence of data gaps and noise.

Contribution

It introduces a novel integrated model that leverages spline interpolation to enhance ARIMA's predictive performance on stock data.

Findings

The combined model outperforms traditional ARIMA in accuracy.

Spline interpolation effectively handles data gaps and noise.

Model provides guidance for short-term stock forecasting.

Abstract

The ARIMA (Autoregressive Integrated Moving Average model) has extensive applications in the field of time series forecasting. However, the predictive performance of the ARIMA model is limited when dealing with data gaps or significant noise. Based on previous research, we have found that cubic spline interpolation performs well in capturing the smooth changes of stock price curves, especially when the market trends are relatively stable. Therefore, this paper integrates the two approaches by taking the time series data in stock trading as an example, establishes a time series forecasting model based on cubic spline interpolation and ARIMA. Through validation, the model has demonstrated certain guidance and reference value for short-term time series forecasting.