# Some examples of application for predicting of compressive sensing method

## TL;DR

This paper evaluates the SALSA algorithm for forecasting electrical signals, temperature, and stock prices, demonstrating its superiority over linear extrapolation and comparing it to causal smoothing extrapolation.

## Contribution

It applies SALSA to diverse real-world data and compares its forecasting performance with existing methods, highlighting its strengths and limitations.

## Key findings

- SALSA outperforms linear extrapolation in all tested cases.
- SALSA provides more accurate range predictions for electrical signals.
- Causal smoothing extrapolation is more conservative for complex systems.

## Abstract

This paper considers application of the SALSA algorithm as a method of forecasting and applies it to simulated electrical signal, temperature recording from the Australian Bureau of Meteorology and stock prices from the Australian stock exchange. It compares it to basic linear extrapolation and casual smoothing extrapolation, in all cases SALSA extrapolation proves to be a better method of forecasting than linear extrapolation. However, it cannot be imperially stated that it is superior to Causal smoothing extrapolation in complex systems as it has a higher L2 euclidean in these experiments. while usually retaining more shape and statistical elements of the original function than Causal smoothing extrapolation. Leading to the conclusion the Causal Smoothing extrapolation can provide a more conservative forecast for complex systems while the SALSA algorithm more accurately predicts the range of possible events as well as being the superior forecasting method for electrical signals, the physical process it is designed to forecast.