  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.
