With the rise of electric vehicles (EVs), new opportunities are emerging, including Vehicle-to-Everything (V2X) technology, which enables EVs to both charge from and discharge to the grid, homes, and other EVs. Leveraging V2X, EVs can act as "batteries-on-wheels," dynamically trading energy to minimize electricity costs and emissions based on real-time energy and mobility forecasts. For households, unlocking these benefits requires smart, automated management of EV charging and discharging. However, designing optimal schedules is a complex task involving dynamic and often uncertain variables such as emission rates, household electricity use, solar generation, electricity prices, and EV travel patterns. To tackle this challenge, we have developed an interactive dashboard that combines forecasting and optimization to support smarter energy decisions. To the best of our knowledge, this is the first system that integrates real-world minute-level forecasts, dynamic scheduling algorithms, and interactive spatiotemporal visualization. It enables users to compare V2X scenarios and explore the impact of forecasting accuracy and configuration strategies over time. The dashboard visualizes key variables and simulates optimal charging and discharging decisions. In this demo, we showcase results from a 31-day simulation at 5-minute intervals, using real-world data to illustrate the impact of various energy management strategies.

Abstract Time series classification (TSC) is a challenging task due to the diversity of types of features that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed. While kernel, neural network, and hybrid approaches perform well overall, some specialized approaches are better suited for specific tasks. In this paper, we propose a new similarity-based classifier, Proximity Forest version 2.0 (PF 2.0), which outperforms previous state-of-the-art similarity-based classifiers across the UCR benchmark and outperforms other state-of-the-art methods on specific datasets in the benchmark that are best addressed by similarity-base methods. PF 2.0 incorporates three recent advances in time series similarity measures — (1) computationally efficient early abandoning and pruning to speedup elastic similarity computations; (2) a new elastic similarity measure, Amerced Dynamic Time Warping ( $${{\,\textrm{ADTW}\,}}$$ ADTW ); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to four and using the first derivative transform with all similarity measures, rather than a limited subset. It also incorporates HYDRA, a dictionary-based transform. We have re-implemented PF 1.0 and implemented PF 2.0 framework in Java, making the PF framework more efficient.

In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging labeled data from a related domain to detect anomalies in an unlabeled target domain. However, existing UDA methods assume consistent anomalous classes across domains. To address this limitation, we propose a novel Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness. Additionally, our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain, ensuring comprehensive feature representation learning and domain-invariant feature extraction. Finally, an effective Center-based Entropy Classifier (CEC) accurately learns normal boundaries in the source domain. Extensive evaluations on multiple real-world datasets and a synthetic dataset highlight DACAD's superior performance in transferring knowledge across domains and mitigating the challenge of limited labeled data in TSAD.

Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks the models to be dominated by higher amplitude ranges, and (3) The absence of explicit segmentation in the continuous-valued sequences which can result in less informative representations. To address these challenges, we introduceEEG2Rep, a self-prediction approach for self-supervised representation learning from EEG. Two core novel components of EEG2Rep are as follows: 1) Instead of learning to predict the masked input from raw EEG, EEG2Rep learns to predict masked input in latent representation space, and 2) Instead of conventional masking methods, EEG2Rep uses a new semantic subsequence preserving (SSP) method which provides informative masked inputs to guide EEG2Rep to generate rich semantic representations. In experiments on 6 diverse EEG tasks with subject variability, EEG2Rep significantly outperforms state-of-the-art methods. We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50% of EEG recordings will result in the most accurate results on all 6 tasks on average. Finally, we show that EEG2Rep is robust to noise addressing a significant challenge that exists in EEG data. Models and code are available at:https://github.com/Navidfoumani/EEG2Rep

Abstract We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of meaningful self-supervised learning tasks that can be defined. Motivated by this insight, we introduce a novel approach called Series2Vec for self-supervised representation learning. Unlike the state-of-the-art methods in time series which rely on hand-crafted data augmentation, Series2Vec is trained by predicting the similarity between two series in both temporal and spectral domains through a self-supervised task. By leveraging the similarity prediction task, which has inherent meaning for a wide range of time series analysis tasks, Series2Vec eliminates the need for hand-crafted data augmentation. To further enforce the network to learn similar representations for similar time series, we propose a novel approach that applies order-invariant attention to each representation within the batch during training. Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series. Additionally, our extensive experiments show that Series2Vec performs comparably with fully supervised training and offers high efficiency in datasets with limited-labeled data. Finally, we show that the fusion of Series2Vec with other representation learning models leads to enhanced performance for time series classification. Code and models are open-source at https://github.com/Navidfoumani/Series2Vec

Abstract Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position encoding in time series analysis is not well-studied and remains controversial, e.g., whether it is better to inject absolute position encoding or relative position encoding, or a combination of them. In order to clarify this, we first review existing absolute and relative position encoding methods when applied in time series classification. We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE). Our new method incorporates the series length and input embedding dimension in absolute position encoding. Additionally, we propose computationally Efficient implementation of Relative Position Encoding (eRPE) to improve generalisability for time series. We then propose a novel multivariate time series classification model combining tAPE/eRPE and convolution-based input encoding named ConvTran to improve the position and data embedding of time series data. The proposed absolute and relative position encoding methods are simple and efficient. They can be easily integrated into transformer blocks and used for downstream tasks such as forecasting, extrinsic regression, and anomaly detection. Extensive experiments on 32 multivariate time-series datasets show that our model is significantly more accurate than state-of-the-art convolution and transformer-based models. Code and models are open-sourced at https://github.com/Navidfoumani/ConvTran .

Many real-world data streams are non-stationary. Subject to concept drift, the distributions change over time. To retain accuracy in the face of such drift, online decision tree learners must discard parts of the tree that are no longer accurate and replace them by new subtrees that reflect the new distribution. The longstanding state-of-the-art online decision tree learner for non-stationary streams is Hoeffding Adaptive Tree (HAT), which adds a drift detection and response mechanism to the classic Very Fast Decision Tree (VFDT) online decision tree learner. However, for stationary distributions, VFDT has been superseded by Extremely Fast Decision Tree (EFDT), which uses a statistically more efficient learning mechanism than VFDT. This learning mechanism needs to be coupled with a compensatory revision mechanism that can compensate for circumstances where the learning mechanism is too eager. The current work develops a strategy to combine the best of both these state-of-the-art approaches, exploiting both the statistically efficient learning mechanism from EFDT and the highly effective drift detection and response mechanism of HAT. To do so requires decoupling of the EFDT splitting and revision mechanisms, as the latter incorrectly triggers the HAT drift detection mechanism. The resulting learner, Extremely Fast Hoeffding Adaptive Tree, responds to drift more rapidly and effectively than either HAT or EFDT, and attains a statistically significant advantage in accuracy even on stationary streams.

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree'', a minor modification to the MOA implementation of Hoeffding Tree---obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

Outlier detection is an important task in data mining. With the growing need to analyze high speed data streams, the task of outlier detection becomes even more challenging as traditional outlier detection techniques can no longer assume that all the data can be stored for processing. While the wellknown Local Outlier Factor (LOF) algorithm has an incremental version (called iLOF), it assumes unbounded memory to keep all previous data points. In this paper, we propose a memory efficient incremental local outlier (MiLOF) detection algorithm for data streams, and a more flexible version (MiLOF F), both have an accuracy close to iLOF but within a fixed memory bound. In addition MiLOF F is robust to changes in the number of data points, underlying clusters and dimensions in the data stream.

Ability to predict the risk of damaging events (e.g. wildfires) is crucial in helping emergency services in their decision making processes, to mitigate and reduce the impact of such events. Today, wildfire rating systems have been in operation extensively in many countries around the world to estimate the danger of wildfires. In this paper we propose a data-driven approach to predict wildfire risk using weather data. We show how we address the inherent challenge arising due to the temporal dynamicity of weather data. Weather observations naturally change in time, with finer-scale variation (e.g. stationary day or night) or large variations (nonstationary day or night), and this determines a temporal variation of the predicted wildfire danger. We show how our dynamic wildfire danger prediction model addresses the aforementioned challenge using context-based anomaly detection techniques. We call our predictive model a Context-Based Fire Risk (CBFR) model. The advantage of our model is that it maintains multiple historical models for different temporal variations (e.g. day versus night), and uses ensemble learning techniques to predict wildfire risk with high accuracy. In addition, it is completely unsupervised and does not rely on expert knowledge, which makes it flexible and easily applied to any region of interest. Our CBFR model is also scalable and can potentially be parallelised to speed up computation. We have considered multiple wildfire locations in the Blue Mountains, Australia as a case study, and compared the results of our system with the existing well-established Australian wildfire rating system. The experimental results show that our predictive model has a substantially higher accuracy in predicting wildfire risk, which makes it an effective model to supplement the operational Australian wildfire rating system.

Outlier detection is an important task in data mining, with applications ranging from intrusion detection to human gait analysis. With the growing need to analyze high speed data streams, the task of outlier detection becomes even more challenging as traditional outlier detection techniques can no longer assume that all the data can be stored for processing. While the well-known Local Outlier Factor (LOF) algorithm has an incremental version, it assumes unbounded memory to keep all previous data points. In this paper, we propose a memory efficient incremental local outlier (MiLOF) detection algorithm for data streams, and a more flexible version (MiLOF_F), both have an accuracy close to Incremental LOF but within a fixed memory bound. Our experimental results show that both proposed approaches have better memory and time complexity than Incremental LOF while having comparable accuracy. In addition, we show that MiLOF_F is robust to changes in the number of data points, the number of underlying clusters and the number of dimensions in the data stream. These results show that MiLOF/MiLOF_F are well suited to application environments with limited memory (e.g., wireless sensor networks), and can be applied to high volume data streams.

Pedestrians movements have a major impact on the dynamics of cities and provide valuable guidance to city planners. In this paper we model the normal behaviours of pedestrian flows and detect anomalous events from pedestrian counting data of the City of Melbourne. Since the data spans an extended period, and pedestrian activities can change intermittently (e.g., activities in winter vs. summer), we applied an Ensemble Switching Model, which is a dynamic anomaly detection technique that can accommodate systems that switch between different states. The results are compared with those produced by a static clustering model (HyCARCE) and also cross-validated with known events. We found that the results from the Ensemble Switching Model are valid and more accurate than HyCARCE.