Raymond P. Burkholder - Things I Do

Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future prediction. While deep learning has achieved major success in time series forecasting, many existing approaches do not explicitly model the dynamics. To bridge this gap, we introduce DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks. Inspired by empirical dynamic modeling (EDM) and rooted in Takens' theorem, DeepEDM presents a novel deep model that learns a latent space from time-delayed embeddings, and employs kernel regression to approximate the underlying dynamics, while leveraging efficient implementation of softmax attention and allowing for accurate prediction of future time steps. To evaluate our method, we conduct comprehensive experiments on synthetic data of nonlinear dynamical systems as well as real-world time series across domains. Our results show that DeepEDM is robust to input noise, and outperforms state-of-the-art methods in forecasting accuracy.

TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness

Time-series forecasting is an essential task with wide real-world applications across domains. While recent advances in deep learning have enabled time-series forecasting models with accurate predictions, there remains considerable debate over which architectures and design components, such as series decomposition or normalization, are most effective under varying conditions. Existing benchmarks primarily evaluate models at a high level, offering limited insight into why certain designs work better. To mitigate this gap, we propose TimeRecipe, a unified benchmarking framework that systematically evaluates time-series forecasting methods at the module level. TimeRecipe conducts over 10,000 experiments to assess the effectiveness of individual components across a diverse range of datasets, forecasting horizons, and task settings. Our results reveal that exhaustive exploration of the design space can yield models that outperform existing state-of-the-art methods and uncover meaningful intuitions linking specific design choices to forecasting scenarios. Furthermore, we release a practical toolkit within TimeRecipe that recommends suitable model architectures based on these empirical insights. The benchmark is available at github.

Malicious node aware wireless multi hop networks: a systematic review of the literature and recommendations for future research

Wireless communication provides great advantages that are not available through their wired counterparts such as flexibility, ease of deployment and use, cost reductions, and convenience. Wireless multi-hop networks (WMN) do not have any centralized management infrastructure. Wireless multi-hop networks have many benefits since proposed. In such networks when a node wants to send a packet to a destination where is not in the transmission range, depend on some intermediate nodes. In this type of networks packet sending is in the form of multiple hop until destination and this work is dynamic. Lack of centralized management cause that some nodes show malicious function. Malicious nodes are that receive packets and drop them maliciously. These malicious nodes could have many reasons such as hardware failure, software failure or lack of power. Such nodes make multiple packets drop from the network and the performance of network strongly decreases. As a result, the throughput of the network decrease, increase end-to-end delay and increase overhead. Therefore, we must aware from presence of malicious node in the network and do routing based on this awareness. Therefore, this paper aims to study and review the present malicious node detection methods that proposed in literatures. We categorized networks in groups, including ad hoc networks, MANET, DTN, Opportunistic networks, WSN, VANET and other wireless networks and compare malicious node detection met