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

ArXiv ID: 2311.10759 “View on arXiv”

Authors: Unknown

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.

Keywords: ARIMA Model, Cubic Spline Interpolation, Time Series Forecasting, Data Imputation, Short-term Prediction, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces moderately advanced mathematical concepts like cubic spline interpolation with a system of equations and ARIMA’s characteristic equations, but lacks backtest-ready specifics such as code, parameter tuning, or robust out-of-sample performance metrics.

  flowchart TD
    A["Research Goal<br>Predict stock market trends<br>with data gaps/noise"] --> B["Data Input<br>Stock Trading Time Series"]
    B --> C["Methodology Step 1<br>Cubic Spline Interpolation<br>for Data Imputation"]
    C --> D["Methodology Step 2<br>ARIMA Modeling<br>on interpolated data"]
    D --> E["Computational Process<br>Model Fitting & Validation"]
    E --> F["Key Findings/Outcomes<br>Integrated model demonstrates<br>guidance for short-term forecasting"]