Applications of band-limited extrapolation to forecasting of weather and financial time series

TL;DR

This paper explores causal extrapolation methods for forecasting weather and financial time series, demonstrating their effectiveness over simple linear methods through simulations and real-world data analysis.

Contribution

It introduces practical applications of band-limited causal extrapolation for forecasting, validated with real data from stock markets and meteorology.

Findings

Causal extrapolation outperforms linear methods in most scenarios

Validated with Australian Stock Exchange and Bureau of Meteorology data

Effective for both weather and financial time series

Abstract

This paper describes the practical application of causal extrapolation of sequences for the purpose of forecasting. The methods and proofs have been applied to simulations to measure the range which data can be accurately extrapolated. Real world data from the Australian Stock exchange and the Australian Bureau of Meteorology have been tested and compared with simple linear extrapolation of the same data. In a majority of the tested scenarios casual extrapolation has been proved to be the more effective forecaster.