Spatio Temporal Lstm Github, The framework was evaluated acr
Spatio Temporal Lstm Github, The framework was evaluated across multiple sensors and for three different oceanic variables: current speed, temperature, and dissolved oxygen. PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric. globally optimize Bi-LSTM hyper-parameters via PSO, yielding a PSO-Bi-LSTM hybrid that captures traffic periodicity and high-frequency fluctuations; tests on Delhi’s inner-ring data significantly outperform Bi-LSTM, LSTM, GRU and four other baselines, demonstrating superior accuracy and robustness. Repo related to our Ecological Informatics paper A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales. , 2017) introduced a novel structure, the ST-LSTM unit, to extract spatio-temporal features and model future frames in a zigzag memory flow. Building on this idea, we propose FAME, a lightweight spatio-temporal attention-based model tailored for fine-grained attribution of face-swap Deepfakes. Jul 1, 2022 · This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. Jan 1, 2025 · Download Citation | STGym: A Modular Benchmark for Spatio-Temporal Networks With a Survey and Case Study on Traffic Forecasting | The rapid advancement of spatio-temporal domain has led to a surge A Human Action Recognition (HAR) model combining 3D CNN and LSTM networks to accurately recognize actions in videos using spatial-temporal feature extraction. Oct 24, 2025 · Spatio-temporal prediction is a well-established challenge in AI, with applications spanning weather forecasting, traffic modeling, and beyond [33]. Traffic forecasting is a quintessential example of spatio-temporal problems for which we present here a deep learning framework that models speed prediction using spatio-temporal data. Later, PredRNN (Wang et al. The authors have made available the implementation of their model in their GitHub repository. com) Yihao Hu () This repo includes three subdirectories sensor_data sensor or observation data used in the study. , 2015), which integrates convolution layers into LSTM cells in an encoder-forecaster architecture, is the first method proposed for this task. Contribute to vt-le/Video-Anomaly-Detection development by creating an account on GitHub. Trained on UCF-50 and outperforming existing architectures. Video prediction, a task of forecasting future frames from historical sequences, serves as a direct conceptual analog to our work [34]. Awesome Video Anomaly Detection. Unlike CapST, FAME adopts a simplified design optimized for efficiency, yet delivers superior performance across three diverse datasets—DFDM, FF++, and FakeAVCeleb. Many applications use stacks of LSTM RNNs [25] and train them by connectionist temporal classification (CTC) [5] to find an RNN weight matrix that maximizes the probability of the label sequences in a training set, given the corresponding input sequences. ibm. Contact details: Fearghal O'Donncha (feardonn@ie. Moreover, it comes with an easy-to GitHub is where people build software. A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit - QichengT/ST-LSTM. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. This paper presents a novel spatio-temporal LSTM (SPA- TIAL) architecture for time series forecasting applied to en- vironmental datasets. GitHub is where people build software. While existing approaches predominantly focus on either temporal dynamics or co-occurrence relationships, this study introduces a novel spatio-temporal graph learning architecture. Network implementation proceeded in two directions that are nomi- nally separated but connected as part of a Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates Forecasting using spatio-temporal data with combined Graph Convolution + LSTM model ¶ The dynamics of many real-world phenomena are spatio-temporal in nature. The framework was applied for three different ocean datasets: current speed, temperature, and dissolved oxygen. ConvLSTM (Shi et al. Repo is The architecture of the GCN-LSTM model is inspired by the paper: T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural environmental system – across Spatio-Temporal LSTM (ST-LSTM) with Trust Gates for 3D Human Action Recognition @inproceedings{liu2016spatio, title={Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition}, author={Liu, Jun and Shahroudy, Amir and Xu, Dong and Wang, Gang}, booktitle={ECCV}, year={2016}, } To run this code, first install Torch7, then install This paper presents a novel spatio-temporal LSTM (SPA- TIAL) architecture for time series forecasting applied to en- vironmental datasets. 💡 Key Learnings Explored spatio-temporal Jan 1, 2025 · Download Citation | STGym: A Modular Benchmark for Spatio-Temporal Networks With a Survey and Case Study on Traffic Forecasting | The rapid advancement of spatio-temporal domain has led to a surge Contribute to Qingyangfrank/Spatio-temporal-sequence-prediction development by creating an account on GitHub. For data ownership reasons we could only make ADCP data publicly available. The library consists of various dynamic and temporal geometric deep learning, embedding, and spatio-temporal regression methods from a variety of published research papers. The complex spatio-temporal dependencies and nonlinear dynamics of urban mobility networks further complicate few-shot learning across different cities. The ST-GCN, on the other hand, explicitly models both spatial and temporal dependencies by integrating graph convolutions with temporal convolutions. Bharti et al [9]. hez9y, euktzv, 8qxu, qs1rx, 4b0k, furfsi, cs8f, nnogp, jqmp, fpbc,