Time Series and Data Science Preprocessing Approaches for Earthquake Analysis

Abstract

Time series are frequently used today to analyze data that changes over time and to predict future trends. Usage areas of time series data include many applications such as financial market forecasts, weather forecasts, sales forecasts, medical diagnostics and stock management. Among the methods, there are techniques such as autoregressive integration, moving average, long-short-term memory neural network, time series condensation, wavelet transform and Frequency Domain. These techniques are chosen depending on the characteristics of the time series data and their intended use. For example, the ARIMA model is used for variable variance and non-stationary time series, while the LSTM model may be more suitable for capturing long-term dependencies. In this article, it has been tried to prove that time series based artificial intelligence systems can be built on fault movements, which are very difficult to predict on earthquake time series data, and it is quite possible to get useful results. In particular, deep learning methods are among the prominent methods in the article. Deep learning methods are used to detect complex structures and analyze large datasets to produce accurate results. These methods include multilayer perceptrons, long-short-term memory neural network, and radial-based function network. It is also emphasized that factors such as the selection of features used in earthquake prediction, data preprocessing, feature engineering and correct model selection are also important. As a result, the use of artificial intelligence techniques on earthquake time series data has great potential in estimating earthquake risk. Deep learning methods perform better, especially for large datasets, and more accurate results can be obtained with the right model selection. However, factors such as data preprocessing and feature selection also need to be considered.