Hybrid wavelet-neural network models for time series

Kılıç, Deniz Kenan; Uğur, Ömür

doi:10.1016/j.asoc.2023.110469

Cited by 9 publications

(7 citation statements)

References 46 publications

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“…On the other hand, apart from deep learning methods, the model that gives the best results is SARIMA. Likewise, in [26], which utilizes LSTM as a deep learning method, SARIMA is the second-best model for prediction of S&P500 and NASDAQ stock prices, which means that statistical models still have an important place with advanced machine learning methods among data-driven models for financial assets.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…Therefore, this study utilizes LSTM, a recurrent neural network (RNN) type model, to predict intraday market prices. One unit LSTM is shown in Figure 4 [26]. The following equations are used to estimate the output h t of the memory cell at time t [46]:…”

Section: Lstmmentioning

confidence: 99%

“…In [26], the long short-term memory (LSTM) network is indicated to be a powerful model in financial time series like stock prices. LSTM, a deep learning model, is also advantageous for EPF due to its ability to effectively capture temporal dependencies, learn from sequential data, handle complex patterns, and adapt to different forecasting horizons [4,5,10,11,13,21,23,[27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

Kılıç,

Nielsen,

Thibbotuwawa

2024

Energies

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For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Section: Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

Kılıç,

Nielsen,

Thibbotuwawa

2024

Energies

Self Cite

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“…Literatures [19][20][21][22] considered incentive mechanisms in FL, literature [19] dynamically allocated a given budget by minimizing the unfairness of the clients' rewards and the waiting time of rewards; Literature [20] proposed a bootstrap truncated gradient Shapley method to reduce the number of times of FL model update and improve computational efficiency; Literature [21] considered the two-level incentive mechanism, added redundant computations to FL through coding technology; Literature [22] designed an incentive mechanism to allocate rewards to clients and model owners (MOs), determined the computational power required for FL subtasks based on rewards, maximized the resource utilization.…”

Section: Incentives Mechanism In Federated Learningmentioning

confidence: 99%

“…All the literature [19][20][21][22] designed incentive mechanisms for FL, but these incentive mechanisms encouraged clients to participate in FL through rewards, rewards are based on clients' contribution, without considering malicious node attacks and how to incentivize clients to honestly participate in FL.…”

Section: Incentives Mechanism In Federated Learningmentioning

confidence: 99%

A Traffic Flow Prediction Method Based on Blockchain and Federated Learning

Zhi,

段,

Yang

2023

Preprint

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Traffic flow prediction is the an important issue in the field of intelligent transportation, and real-time and accurate traffic flow prediction plays a crucial role in improving the efficiency of traffic networks. Existing traffic flow prediction methods use deep learning models and collected traffic flow datasets to predict traffic flow. These datasets contain the private data of clients, so if some clients are unwilling to participate in the traffic flow prediction, the traffic flow prediction results will be inaccurate. Therefore, it is important to address the issue that how to motivate clients to actively participate in the traffic flow prediction while protecting the privacy data. So, this paper proposes a traffic flow prediction method based on blockchain and federated learning (TFPM-BFL). Firstly, the traffic flow prediction problem is described as federated learning (FL) task, the improved long and short-term memory (LSTM) model is used to predict the traffic flow at the client side, the traffic flow data is decomposed by wavelet function, and the LSTM network with added attention mechanism is used to obtain traffic flow prediction results; Then, incentive mechanism based on reputation value is proposed, the model parameters are uploaded to the blockchain for local and partial reputation evaluation through smart contracts, and the corresponding global reputation update is obtained, the reward is distributed to clients according to global reputation, so the clients are motivated to participate in the traffic flow prediction; Finally, the model aggregation method based on reputation value and compression rate is designed. Based on the reputation evaluation results, the edge server uses the Topk algorithm to perform high-quality aggregation of the local model parameters uploaded by clients (roadside units), central server aggregates the partial model parameters from edge server, and then the central server distributes the global aggregated model parameters to clients to perform the next round of FL. By using the FL framework, TFPM-BFL uploads the model parameters instead of the original traffic flow data, so it can protect private data. Moreover, it can provide incentive mechanism through reputation evaluation and reward to encourage clients to participate in the FL task. Simulation results show that TFPM-BFL can realize accurate and timely traffic flow prediction, and it can effectively motivate clients to participate in FL task while ensuring the privacy of the underlying data.

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