Dilated convolutional neural networks for time series forecasting

Borovykh, Anastasia; Bohté, Sander M.; Oosterlee, Cornelis W.

doi:10.21314/jcf.2019.358

Cited by 95 publications

(78 citation statements)

References 14 publications

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“…In the literature, different stock market index data were used for the experiments. [123,124,125,126,127,128,129,130,131,132,133,134,114] used S&P500 as their dataset. The authors of [123,124,135,136,137] used NIKKEI as their dataset.…”

Section: Index Forecastingmentioning

confidence: 99%

“…In [138], genetic DNN was used for DJIA index forecasting. The authors of [127] proposed a new DNN model which is called Wavenet convolutional net for time series forecasting. The authors of [148] proposed a (Threshold Autoregressive (TAR)-Vector Error Correction model (VEC)-Recurrent Hybrid Elman (RHE)) model for forex and stock index of return prediction and compared several models.…”

Section: Index Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

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294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Index Forecastingmentioning

confidence: 99%

Section: Index Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

728

294

View full text Add to dashboard Cite

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“…Furthermore, they present other advantages over RNNs such as lower memory requirements, parallel processing of long sequences as opposed to the sequential approach of RNNs, and a more stable training scheme. Several works have already successfully used TCNs for time series forecasting tasks: the original architecture using stacked dilated convolutions was proposed in [25] to improve the performance of LSTM networks for financial domain problems; Ref. [26] designed a deep TCN for multiple related time series with an encoder-decoder scheme, evaluating over data from the sales domain; the study in [27] proposed a multivariate time series forecasting model for meteorological data, which outperformed several popular deep learning models.…”

Section: Introductionmentioning

confidence: 99%

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

et al. 2020

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Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

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“…This technique was introduced by [56] to expand the size of CNN's receptive field of images without modifying the data structure. The same technique can be applied to the time series data [57,58] to expand the data length effectively without increasing the neural network structure, as shown in Figure 4c. Due to its sparse connection and weight sharing mechanism, dilated convolution networks can automatically learn translationally-invariant features from longer input time series while having fewer trainable parameters than conventional CNN.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Kumar

Tzou

2020

Energies

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Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

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Dilated convolutional neural networks for time series forecasting

Cited by 95 publications

References 14 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

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