Temporal Prediction Research Articles

Null effects of temporal prediction on recognition memory but evidence for differential neural activity at encoding. A registered report

Previous research has demonstrated that rhythmic presentation of stimuli during encoding boosts subsequent recognition and is associated with distinct neural activity compared with when stimuli are presented in an arrhythmic manner. However, it is unclear whether the effect is driven by automatic entrainment to rhythm or non-rhythmic temporal prediction. This registered report presents an Electroencephalographic (EEG) study aimed at establishing the cognitive and neural mechanisms of the effect of temporal prediction on recognition. In a blocked design, stimulus onset during encoding was systematically manipulated in four conditions prior to recognition testing: rhythmic fixed (RF), rhythmic variable (RV), arrhythmic fixed (AF), and arrhythmic variable (AV). By orthogonally varying rhythm and temporal position we were able to assess their independent contributions to recognition enhancement. Our behavioural results did not replicate previous findings that show a difference in recognition memory based on temporal predictability at encoding. However, event-related potential (ERP) component analysis did show an early (N1) interaction effect of temporal position and rhythm, and later (N2 and Dm) effects driven by temporal position only. Taken together, we observed effects of temporal prediction at encoding, but these differences did not translate to later effects of memory, suggesting that effects of temporal prediction on recognition are less robust than previously thought.

Disk failure prediction based on association analysis and SSA-LSTM

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method.

Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac.

Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. Experiments were performed on a prototype 1.0T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. The end-to-end latency of the system was measured to be (389 ± 15)ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3)mm, compared to the (3.2 ± 1.9)mm of the offline LSTM, the (3.3 ± 1.4)mm of the online LR and the (4.4 ± 2.4)mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy withMLC-tracking.

Short-Term Demand Prediction for On-Demand Food Delivery with Attention-Based Convolutional LSTM

Demand prediction for on-demand food delivery (ODFD) is of great importance to the operation and transportation resource utilization of ODFD platforms. This paper addresses short-term ODFD demand prediction using an end-to-end deep learning architecture. The problem is formulated as a spatial–temporal prediction. The proposed model is composed of convolutional long short-term memory (ConvLSTM), and convolutional neural network (CNN) units with encoder–decoder structure. Specifically, long short-term memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. The convolution unit is responsible for capturing spatial attributes, while the LSTM part is adopted to learn temporal attributes. Additionally, an attentional model is designed and integrated to enhance the prediction performance by addressing the spatial variation in demand. The proposed approach is compared to several baseline models using a historical ODFD dataset from Shenzhen, China. Results indicate that the proposed model obtains the highest prediction accuracy by capturing both spatial and temporal correlations with attention information focusing on different parts of the input series.

Beyond the ears: A review exploring the interconnected brain behind the hierarchical memory of music.

Music is a ubiquitous element of daily life. Understanding how music memory is represented and expressed in the brain is key to understanding how music can influence human daily cognitive tasks. Current music-memory literature is built on data from very heterogeneous tasks for measuring memory, and the neural correlates appear to differ depending on different forms of memory function targeted. Such heterogeneity leaves many exceptions and conflicts in the data underexplained (e.g., hippocampal involvement in music memory is debated). This review provides an overview of existing neuroimaging results from music-memory related studies and concludes that although music is a special class of event in our lives, the memory systems behind it do in fact share neural mechanisms with memories from other modalities. We suggest that dividing music memory into different levels of a hierarchy (structural level and semantic level) helps understand overlap and divergence in neural networks involved. This is grounded in the fact that memorizing a piece of music recruits brain clusters that separately support functions including-but not limited to-syntax storage and retrieval, temporal processing, prediction versus reality comparison, stimulus feature integration, personal memory associations, and emotion perception. The cross-talk between frontal-parietal music structural processing centers and the subcortical emotion and context encoding areas explains why music is not only so easily memorable but can also serve as strong contextual information for encoding and retrieving nonmusic information in our lives.

FPGN: follower prediction framework for infectious disease prevention

In recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on (alpha , beta )-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62.

"What" and "when" predictions modulate auditory processing in a mutually congruent manner.

Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if "beat" timing predictions selectively interact with "what" predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates. To address these questions, our study manipulated "what" and "when" predictions at different levels - (local) interval-defining and (global) beat-defining - within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task. Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of "what" predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of "what" and "when" predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network. Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing.

Deep Autoencoder-like non-negative matrix factorization with graph regularized for link prediction in dynamic networks

Link prediction in dynamic(temporal) networks refers to predicting future edges by analyzing the available network information. Among the existing temporal link prediction approaches, non-negative matrix factorization(NMF) is a kind of competitive algorithm and has attracted extensive attention. However, traditional NMF-based prediction methods are shallow methods and cannot fully mine the dynamic network, which may lead to a decrease in performance of algorithms. To overcome these shortcomings, inspired by deep Autoencoder, we propose two novel deep Autoencoder-like NMF with graph regularized prediction methods for dynamic networks. By fusing encoder component with deep structure into deep NMF model, our algorithms can sufficiently exploit the complex hierarchical information hidden in dynamic networks. To further extract the abundant information hidden in dynamic networks, graph regularization and PageRank are utilized to exploit the local and global topology information of each snapshot, respectively. By jointly optimizing them in deep Autoencoder-like NMF model, our model is able to preserve the local and global information hidden in dynamic networks, simultaneously. Moreover, an effective alternating iterative method with convergence guarantee is developed for minimizing the established model. Finally, we test our proposed prediction methods on several synthetic and real world datasets to demonstrate that our approaches outperform the state-of-the-art prediction approaches.

The Pleasurable Urge to Move to Music Through the Lens of Learning Progress.

Interacting with music is a uniquely pleasurable activity that is ubiquitous across human cultures. Current theories suggest that a prominent driver of musical pleasure responses is the violation and confirmation of temporal predictions. For example, the pleasurable urge to move to music (PLUMM), which is associated with the broader concept of groove, is higher for moderately complex rhythms compared to simple and complex rhythms. This inverted U-shaped relation between PLUMM and rhythmic complexity is thought to result from a balance between predictability and uncertainty. That is, moderately complex rhythms lead to strongly weighted prediction errors which elicit an urge to move to reinforce the predictive model (i.e., the meter). However, the details of these processes and how they bring about positive affective responses are currently underspecified. We propose that the intrinsic motivation for learning progress drives PLUMM and informs the music humans choose to listen to, dance to, and create. Here, learning progress reflects the rate of prediction error minimization over time. Accordingly, reducible prediction errors signal the potential for learning progress, producing a pleasurable, curious state characterized by the mobilization of attentional and memory resources. We discuss this hypothesis in the context of current psychological and neuroscientific research on musical pleasure and PLUMM. We propose a theoretical neuroscientific model focusing on the roles of dopamine and norepinephrine within a feedback loop linking prediction-based learning, curiosity, and memory. This perspective provides testable predictions that will motivate future research to further illuminate the fundamental relation between predictions, movement, and reward.

Both stronger and weaker cerebro-cerebellar functional connectivity patterns during processing of spoken sentences in autism spectrum disorder.

Cerebellar differences have long been documented in autism spectrum disorder (ASD), yet the extent to which such differences might impact language processing in ASD remains unknown. To investigate this, we recorded brain activity with magnetoencephalography (MEG) while ASD and age-matched typically developing (TD) children passively processed spoken meaningful English and meaningless Jabberwocky sentences. Using a novel source localization approach that allows higher resolution MEG source localization of cerebellar activity, we found that, unlike TD children, ASD children showed no difference between evoked responses to meaningful versus meaningless sentences in right cerebellar lobule VI. ASD children also had atypically weak functional connectivity in the meaningful versus meaningless speech condition between right cerebellar lobule VI and several left-hemisphere sensorimotor and language regions in later time windows. In contrast, ASD children had atypically strong functional connectivity for in the meaningful versus meaningless speech condition between right cerebellar lobule VI and primary auditory cortical areas in an earlier time window. The atypical functional connectivity patterns in ASD correlated with ASD severity and the ability to inhibit involuntary attention. These findings align with a model where cerebro-cerebellar speech processing mechanisms in ASD are impacted by aberrant stimulus-driven attention, which could result from atypical temporal information and predictions of auditory sensory events by right cerebellar lobule VI.

Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach.

Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency.

Temporal prediction of the behavior between the number of leukocytes and CD4 + T lymphocytes in HIV positive patients with antiretroviral therapy

Introducción: El elevado costo de la citometría de flujo para el seguimiento de los valores de CD4+ en pacientes con VIH ha justificado la búsqueda de metodologías predictivas de este valor, que contribuyan a simplificar y reducir los costos del seguimiento terapéutico de los pacientes con el VIH/SIDA. Objetivo: establecer las posibles correspondencias matemáticas predictivas entre el recuento de CD4+ &gt; 500 células/μL3 y la cantidad de leucocitos/μL3. Metodología: Se realizó una inducción matemática con muestras en el tiempo de ocho pacientes, para establecer un patrón predictivo entre el recuento de leucocitos presentes en un recuento de CD4+ &gt; 500 células/μL3. La validez del patrón predictivo fue confirmada en 62 muestras a las cuales les fue previamente enmascarado el recuento de CD4+/μL3. Posteriormente fue calculada la probabilidad de acierto del patrón establecido, así como la sensibilidad y especificidad. Resultados: el patrón matemático de las ocho muestras reveló que, para un recuento de CD4+ &gt; 500 células/μL3, se presenta un valor mayor de 3.7 leucocitos/μL3, con valor de 1 al calcular la probabilidad y, del 100% al calcular la sensibilidad y especificidad en la muestra restante. Conclusiones: el presente estudio reveló un orden matemático determinista a partir del cual es posible establecer correspondencias directas entre el recuento de leucocitos leucocitos/μL3 mayores a 3.7 y CD4+ mayores a 500 células/μL3 en el contexto de la teoría de la probabilidad.

High-Quality Temporal Link Prediction for Weighted Dynamic Graphs via Inductive Embedding Aggregation

Temporal link prediction (TLP) is an inference task on dynamic graphs that predicts future topology using historical graph snapshots. Existing TLP methods are usually designed for unweighted graphs with fixed node sets. Some of them cannot be generalized to the prediction of weighted graphs with non-fixed node sets. Although several methods can still be used to predict weighted graphs, they can only derive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low-quality</i> prediction snapshots sensitive to large edge weights but fail to distinguish small and zero weights in adjacency matrices. In this study, we consider the challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-quality</i> TLP on weighted dynamic graphs and propose a novel inductive dynamic embedding aggregation (IDEA) method, inspired by the high-resolution video prediction. IDEA combines conventional error minimization objectives with a scale difference minimization objective, which can generate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-quality</i> weighted prediction snapshots, distinguishing differences among large, small, and zero weights in adjacency matrices. Since IDEA adopts an inductive dynamic embedding scheme with an attentive node aligning unit and adaptive embedding aggregation module, it can also tackle the TLP on weighted graphs even with non-fixed node sets. Experiments on datasets of various scenarios validate that IDEA can derive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-quality</i> prediction results for weighted dynamic graphs and tackle the variation of node sets.

MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST

Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST data. However, global SST is a unified system of high regionality, and the SST in different sea areas shows different changing patterns due to the influence of various factors, e.g., geographic location, ocean currents and sea depth. Without a good understanding of such regionality of SST, we cannot quantitatively integrate the regionality information of SST into SST prediction models to make them adaptive to different SST patterns around the world and improve the prediction accuracy. To address this issue, we proposed the Multi-Stage Spatio–Temporal Clustering (MuSTC) method to quantitatively identify sea areas with similar SST patterns. First, MuSTC sequentially learns the representation of long-term SST with a deep temporal encoder and calculates the spatial correlation scores between grid ocean regions with self-attention. Then, MuSTC clusters grid ocean regions based on the original SST data, encoded long-term SST representation and spatial correlation scores, respectively, to obtain the sea areas with similar SST patterns from different perspectives. According to the experiments in three ocean areas, i.e., the North Pacific Ocean (NPO), the South Atlantic Ocean (SAO) and the North Atlantic Ocean (NAO), the clustering results generally match the distribution of ocean currents, which demonstrates the effectiveness of our MuSTC method. In addition, we integrate the clustering results into two representative spatio–temporal prediction models, i.e., Spatio–Temporal Graph Convolutional Networks (STGCN) and Adaptive Graph Convolutional Recurrent Network (AGCRN), to conduct SST prediction. According to the results of experiments, the integration of regionality information leads to the reduction of Root Mean Square Error (RMSE) by 1.95%, 1.39% and 1.28% in NPO, SAO and NAO, respectively, using the STGCN model, and the reduction of RMSE by 4.94%, 0.74% and 1.43% by using the AGCRN model. Such results indicate that the integration of regionality information could notably improve the prediction accuracy of SST.

Multi-Scale Spatial–Temporal Transformer: A Novel Framework for Spatial–Temporal Edge Data Prediction

Spatial–temporal prediction is an important part of a great number of applications, such as urban traffic control, urban traffic management, and urban traffic planning. However, real-world spatial–temporal data often have complex patterns, so it is still challenging to predict them accurately. Most existing spatial–temporal prediction models fail to aggregate the spatial features in a suitable neighborhood during fixed spatial dependencies extraction and lack adequately comprehensive time series analysis for intricate temporal dependencies. This paper proposes a novel model named multi-scale spatial–temporal transformer network (MSSTTN) to deal with intricate spatial–temporal patterns. Firstly, we develop an improved graph wavelet neural network, which learns how to pass the spatial graph signals of different frequency scales to adjust the neighborhood of feature aggregation adaptively. Then, we propose decomposing the time series into local trend-cyclical parts of various scales during time series analysis, making the model capture more reliable temporal dependencies. The proposed model has been evaluated on publicly available real-world datasets. The experimental findings indicate that the proposed model exhibits superior performance compared to conventional techniques including, spatial–temporal transformer (STTNs), GraphWaveNet, and others.

Spatial and temporal distribution characteristics and prediction analysis of nitrogen and phosphorus surface source pollution in Shandong Province under the climate and land use changes

The aim of this study was to explore the characteristics of non-point source pollution of nitrogen (N) and phosphorus (P) under the background of climate and land use in Shandong Province. First, using the InVEST NDR module in the model, N and P non-point source pollution in 2010 and 2020 in Shandong Province were simulated; then, based on precipitation data under three different global climate models (MRI-ESM-0, GFDL-ESM4, and Ec-Earth3) and two Shared Socioeconomic Pathways (SSP245 and SSP585), land use data under two Shared Socioeconomic Pathways (SSP245 and SSP585) were used to simulate and predict the non-point source pollution of N and P in Shandong Province in 2030. The results showed the following: (1) On the time scale, the output load and total output of N and P decreased during 2010–2020, while the output load and total output of N and P increased during 2020–2030. (2) On the spatial scale, the spatial distribution of N and P output loads in 2010, 2020, and 2030 is roughly the same, being “low in the northwest and high in the southeast”. (3) Different climate scenarios have a great influence on N and P output load and total output, and the N and P pollution in the SSP585 scenario is more serious. The total output of N and P did not change much in different climate models, while the spatial distribution of the output load of N and P varied significantly, indicating that different climate models had a greater impact on the spatial distribution of the output load of N and P. (4) The overall cold hot spot pattern of nitrogen and phosphorus pollution in Shandong Province is stable, basically showing a “band + cluster + scatter” distribution pattern; the hot spot area in the central and southern region of Shandong Province changes little regardless of the model, the northwest is basically a cold spot area, and the nitrogen and phosphorus hot spot area under the SSP245 scenario in Ec-Earth3 model had the least amount of change. According to research results, combined with the actual situation of Shandong Province, it is hoped that it can provide theoretical basis for the prevention and control of non-point source pollution in Shandong Province in the future.

Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks.

A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the reward prediction error, which resembles dopamine neuron firing, to learn the best action in response to a set of cues. Though these models can explain many facets of behavior, reproducing some types of goal-directed behavior, such as renewal and reversal, require additional model components. Here we present a reinforcement learning model, TD2Q, which better corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing indirect pathway neurons (N). Unlike previous two-Q architectures, a novel and critical aspect of TD2Q is to update the G and N matrices utilizing the temporal difference reward prediction error. A best action is selected for N and G using a softmax with a reward-dependent adaptive exploration parameter, and then differences are resolved using a second selection step applied to the two action probabilities. The model is tested on a range of multi-step tasks including extinction, renewal, discrimination; switching reward probability learning; and sequence learning. Simulations show that TD2Q produces behaviors similar to rodents in choice and sequence learning tasks, and that use of the temporal difference reward prediction error is required to learn multi-step tasks. Blocking the update rule on the N matrix blocks discrimination learning, as observed experimentally. Performance in the sequence learning task is dramatically improved with two matrices. These results suggest that including additional aspects of basal ganglia physiology can improve the performance of reinforcement learning models, better reproduce animal behaviors, and provide insight as to the role of direct- and indirect-pathway striatal neurons.

Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning

The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems.

Remaining cycle time prediction: Temporal loss functions and prediction consistency

The usefulness of remaining cycle time models for predictive and prescriptive process monitoring depends not only on the overall accuracy of the predictions but also on their earliness: predictions should be as accurate as possible, as early as possible. To give this criterion more weight in model fitting, the paper evaluates three L1 loss functions with temporal decay. All have the property of increasing the weight of residuals from the early stages of a process relative to residuals from later stages but do so to different degrees. The loss functions are used in LSTM networks for training remaining throughout time models of four different business processes based on publicly available event log data sets. Compared to models trained with unweighted L1 loss, the suggested modifications yield small but significant improvements in earliness on out-of-sample data. Neither the unweighted L1 loss nor the modifications led to models with strictly monotonically decreasing predictions of the remaining time.

VAM: An End-to-End Simulator for Time Series Regression and Temporal Link Prediction in Social Media Networks

We present a machine-learning-driven end-to-end simulator, called the <i>Volume-Audience-Match</i> (VAM) simulator. VAM&#x2019;s purpose is to simulate future phenomena related to various topics of discussion in social media networks. We focus our attention on the social media platform, Twitter, due to its abundant use in today&#x2019;s world. VAM was applied to do time series forecasting to predict the future: 1) number of total activities; 2) number of active old users; and 3) number of newly active users over the span of 24 hours from the start time of prediction. VAM then used these macroscopic volume predictions (VPs) to perform user link predictions. A user&#x2013;user edge was assigned to each of the activities in the 24 future time steps. We report that VAM outperformed multiple baseline models in the time series task, which were the auto-regressive integrated moving average (ARIMA), auto regressive moving average (ARMA), auto regressive (AR), moving average (MA), Persistence Baseline, and state-of-the-art tNodeEmbed models. Furthermore, we show that VAM outperformed the Persistence Baseline and tNodeEmbed models used for the user-assignment tasks. Finally, it is also shown that using Reddit activity data improves prediction accuracy.