Short-term daily precipitation forecasting with seasonally-integrated autoencoder

Abstract

Short-term precipitation forecasting is essential for planning of human activities in multiple scales, ranging from individuals' planning, urban management to flood prevention. Yet the short-term atmospheric dynamics are highly nonlinear that it cannot be easily captured with classical time series models. On the other hand, deep learning models are good at learning nonlinear interactions, but they are not designed to deal with the seasonality in time series. In this study, we aim to develop a forecasting model that can both handle the nonlinearities and detect the seasonality hidden within the daily precipitation data. To this end, we propose a seasonally-integrated autoencoder (SSAE) consisting of two long short-term memory (LSTM) autoencoders: one for learning short-term dynamics, and the other for learning the seasonality in the time series. Our experimental results show that not only does the SSAE outperform various time series models regardless of the climate type, but it also has low output variance compared to other deep learning models. The results also show that the seasonal component of the SSAE helped improve the correlation between the forecast and the actual values from 4% at horizon 1 to 37% at horizon 3.

arXiv comment35 pages, 13 figures

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Sequence to Sequence Learning with Neural Networks
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• WaveNet: A Generative Model for Raw Audio
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• Unsupervised Learning of Video Representations using LSTMs
• On the Variance of the Adaptive Learning Rate and Beyond
• Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
• A Multi-Horizon Quantile Recurrent Forecaster
• DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data