|Project Description: ||In any research based on data, having accurate data is essential; as data becomes less accurate, it becomes less useful. One of the forms inaccurate data can take is that of an accidental error, or spike, where the abnormal value of a specific data point deviates
significantly from the values of its surrounding data points. In this project, I implemented two signal-processing algorithms to identify data
spikes in large sets of water level data. To verify the accuracy of my method, I implemented a spiked data generator and used the results of which to test the different spike detection algorithms. I then compared the results of both methods against each other.