STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting (1912.00134v4)

Abstract: Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has become a relevant area of investigation. These works apply either recurrent neural networks (RNN) or some hybrid approach mixing RNN and convolutional neural networks (CNN). In this work, we propose STConvS2S (Spatiotemporal Convolutional Sequence to Sequence Network), a deep learning architecture built for learning both spatial and temporal data dependencies using only convolutional layers. Our proposed architecture resolves two limitations of convolutional networks to predict sequences using historical data: (1) they violate the temporal order during the learning process and (2) they require the lengths of the input and output sequences to be equal. Computational experiments using air temperature and rainfall data from South America show that our architecture captures spatiotemporal context and that it outperforms or matches the results of state-of-the-art architectures for forecasting tasks. In particular, one of the variants of our proposed architecture is 23% better at predicting future sequences and five times faster at training than the RNN-based model used as a baseline.

• The paper introduces a novel deep learning architecture, STConvS2S, which uses causal and reversed convolutional blocks to effectively capture spatiotemporal dependencies in weather data.
• It overcomes traditional CNN limitations by maintaining temporal order and extending prediction lengths with a dedicated Temporal Generator Block.
• Experimental results demonstrate that STConvS2S outperforms ARIMA and RNN-based models, achieving faster training times and improved RMSE on datasets like CFSR and CHIRPS.

STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting

The paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting" introduces a novel deep learning architecture aimed at improving the accuracy of weather prediction through spatiotemporal data analysis. This is particularly significant in the field of geosciences where understanding the stochastic behavior of meteorological phenomena is pivotal for accurate forecasting. The authors propose a sequence-to-sequence model, STConvS2S, which leverages the power of Convolutional Neural Networks (CNN) to address two significant limitations of traditional convolutional approaches: the violation of temporal order and the requirement for matching input-output sequence lengths.

Key Contributions and Architecture

STConvS2S is distinctly formulated to capture both spatial and temporal dependencies using exclusively convolutional layers, avoiding recurrent networks typically employed in such tasks. This choice aims to combine computational efficiency with effective representation learning for spatiotemporal datasets, a departure from prior hybrid CNN-RNN approaches like ConvLSTM.

The authors introduce two variants of the STConvS2S architecture to ensure causality:

1. Temporal Causal Block: Incorporates causal convolutions ensuring the model respects the temporal order during learning, avoiding data leakage of future information.
2. Temporal Reversed Block: Offers an alternative approach that reverses the sequence order via a linear transformation, ensuring no future information is included during the learning process.

In addition to handling temporal constraints, STConvS2S leverages a Temporal Generator Block designed to address the issue of sequence length limitation. This block effectively extends output sequences, allowing flexible prediction horizons beyond the input sequence length, showcasing adaptability in multi-step forecasting tasks.

Experimental Evaluation

The paper demonstrates the efficacy of STConvS2S against traditional statistical models like ARIMA and state-of-the-art RNN-based models such as ConvLSTM, PredRNN, and MIM on datasets including CFSR and CHIRPS. The experimental results show that STConvS2S not only matches but often surpasses these models in predictive performance and computational efficiency.

Particularly noteworthy is the performance gain involved with the temporal reversed block variant (STConvS2S-R), which achieves significant improvements in RMSE and is notably faster, training up to five times faster than RNN-based counterparts. This marks a substantial stride towards efficient processing and prediction in spatiotemporal tasks.

Implications and Future Directions

The paper's contributions shift the paradigm in weather forecasting models by highlighting the potential of purely convolutional architectures to handle spatiotemporal dependencies effectively. The ability to forecast longer sequences presents practical implications in improving meteorological predictions, crucial for decision-making in sectors like agriculture, aviation, and disaster preparedness.

Moving forward, future research can explore techniques to mitigate errors, particularly in datasets characterized by high variability such as rainfall data. Additionally, there is ample scope for extending the architecture's application in other domains where spatiotemporal forecasting is relevant, offering exciting possibilities for interdisciplinary advancements.

The paper provides valuable insights into enhancing convolutional approaches for sequence modeling, encouraging further exploration and comparison with recurrent architectures. The STConvS2S architecture represents a step forward in the pursuit of efficient and accurate spatiotemporal data modeling.