Research Area: Machine Learning
Multi-horizon time series forecasting is a very challenging task in many fields of research. In the field of machine learning, artificial neural networks have been used to carry out these tasks. However, there are still problems that are of general interest to researchers such as: Loss of data in data acquisition and long-term forecast. In this paper, we propose a hybrid Meta-Transfer Learning technique based on transfer-learning, meta-learning and signal detection by means of the discrete wavelet transform to solve the aforementioned problems in multi-horizon time series forecasting. Input-to-state stability analysis and the strong and weak convergence analysis for the proposed method are included. To validate the effectiveness of the method, the long-term prediction of earthquakes magnitude (M>4.5) in Italy is taken as a case of study, using information from Italy and Mexico. Simulations of classic methods for forecasting time series based on neural models are performed. The forecasting performance obtained is the minimum square error (MSE) is 0.091, while for the meta-transfer learning, the MSE is 0.032.
Keywords:
Forecasting
Time series analysis
Neural networks
Predictive models
Earthquakes
Discrete wavelet transforms
Task analysis
Author(s) Name: Mario Maya; Wen Yu
Journal name: IEEE Access
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/ACCESS.2022.3159797
Volume Information: Volume: 10
