Temporal Convolutional Networks for the Advance Prediction of ENSO

Yan, Jining; Mu, Lin; Wang, Lizhe; Ranjan, Rajiv; Zomaya, Albert Y.

doi:10.1038/s41598-020-65070-5

Cited by 212 publications

(111 citation statements)

References 48 publications

(42 reference statements)

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“…The experiments showed that the model using TCN produced better forecasting compared to other classic machine learning approaches. As the El Niño-Southern Oscillation (ENSO) prediction accuracy of current popular numerical methods was not high, Yan et al [47] proposed an ensemble empirical mode decomposition and TCN hybrid approach to obtain ENSO prediction results and the correlation coefficient between the worst predicted southern oscillation index and the actual value reached 0.64.…”

Section: Other Potential Deep Learning Methodsmentioning

confidence: 99%

Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

et al. 2021

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Satellite altimetry and tide gauges are the two main techniques used to measure sea level. Due to the limitations of satellite altimetry, a high-quality unified sea level model from coast to open ocean has traditionally been difficult to achieve. This study proposes a fusion approach of altimetry and tide gauge data based on a deep belief network (DBN) method. Taking the Mediterranean Sea as the case study area, a progressive three-step experiment was designed to compare the fused sea level anomalies from the DBN method with those from the inverse distance weighted (IDW) method, the kriging (KRG) method and the curvature continuous splines in tension (CCS) method for different cases. The results show that the fusion precision varies with the methods and the input measurements. The precision of the DBN method is better than that of the other three methods in most schemes and is reduced by approximately 20% when the limited altimetry along-track data and in-situ tide gauge data are used. In addition, the distribution of satellite altimetry data and tide gauge data has a large effect on the other three methods but less impact on the DBN model. Furthermore, the sea level anomalies in the Mediterranean Sea with a spatial resolution of 0.25° × 0.25° generated by the DBN model contain more spatial distribution information than others, which means the DBN can be applied as a more feasible and robust way to fuse these two kinds of sea levels.

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Section: Other Potential Deep Learning Methodsmentioning

confidence: 99%

Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

et al. 2021

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“…Recent work suggests that ML approaches can predict the Nino3.4 index more skillfully than dynamical forecast systems with lead times of more than a year (Yan et al, 2020, He and Eastman 2020, Ham et al 2019, Dijkstra et al 2019, largely using different types of neural networks, for example, deep neural networks, convolution neural networks (CNN), and recurrent neural networks like the convolution long short-term memory. The lack of sufficient observational data was overcome in one study (Ham et al 2019) by using Earth system model (ESM) simulations: a CNN network was trained with global SST and heat content data from historical simulations of 21 CMIP5 models to predict the Nino3.4 index.…”

Section: Rationalementioning

confidence: 99%

Assessing Teleconnections-Induced Predictability of Regional Water Cycle on Seasonal to Decadal Timescales Using Machine Learning Approaches

Mahajan

2021

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“…However, the energy sector applications of such technology are still barely studied. R. [21]. In this research, a comparative experiment was carried out between LSTM and TCN, where TCN outperforms LSTM using various time-series datasets.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid

2021

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The prediction of time series data applied to the energy sector (prediction of renewable energy production, forecasting prosumers’ consumption/generation, forecast of country-level consumption, etc.) has numerous useful applications. Nevertheless, the complexity and non-linear behaviour associated with such kind of energy systems hinder the development of accurate algorithms. In such a context, this paper investigates the use of a state-of-art deep learning architecture in order to perform precise load demand forecasting 24-h-ahead in the whole country of France using RTE data. To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms. WaveNet uses dilated causal convolutions and skip-connection to utilise long-term information. This kind of novel ML architecture presents different advantages regarding other statistical algorithms. On the one hand, the proposed deep learning model’s training process can be parallelized in GPUs, which is an advantage in terms of training times compared to recurrent networks. On the other hand, the model prevents degradations problems (explosions and vanishing gradients) due to the residual connections. In addition, this model can learn from an input sequence to produce a forecast sequence in a one-shot manner. For comparison purposes, a comparative analysis between the most performing state-of-art deep learning models and traditional statistical approaches is presented: Autoregressive-Integrated Moving Average (ARIMA), Long-Short-Term-Memory, Gated-Recurrent-Unit (GRU), Multi-Layer Perceptron (MLP), causal 1D-Convolutional Neural Networks (1D-CNN) and ConvLSTM (Encoder-Decoder). The values of the evaluation indicators reveal that WaveNet exhibits superior performance in both forecasting accuracy and robustness.

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Temporal Convolutional Networks for the Advance Prediction of ENSO

Cited by 212 publications

References 48 publications

Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

Assessing Teleconnections-Induced Predictability of Regional Water Cycle on Seasonal to Decadal Timescales Using Machine Learning Approaches

Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid

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