Time series analysis has wide applications in many fields such as economics, finance, computer sciences, engineering, and earth sciences, where understanding patterns and trends is important to decision making and predicting future behaviours. For example, the ground motion caused by an earthquake is recorded as a course of time. Time-series data are always noisy and high-dimensional. There features increase the difficulty of deep and precise analysis. Recent development in deep learning provides some new techniques for efficient time-series analysis. In this project, we aim to create new models of unsupervised learning of features for time series analysis and prediction. Applications into seismic data for earthquake identification & prediction and for petroleum exploration will be explored.
- All the downloaded data is available as 4 different Kaggle datasets, and the links to the datasets can be found below:
- All the pre-processed data is available as 2 different Kaggle datasets, and the links to the datasets can be found below:
- The rest of the details about the project can be found in my project report published at Notion. You can check it out here. It includes details such as:
- Study region
- Data types considered
- Distribution and statistics of the downloaded data
- Distribution and statistics of the retained data
- Differences beteween traditional and machine learning algorithms for automatical picking of phase
- And much more!