Time-series anomaly detection is critical for numerous real-world applications and has been extensively studied. However, existing methods are typically designed to identify anomalies within a complete time series. In other words, they rely on access to ground truth data to calculate anomaly scores and distinguish anomalous data from normal patterns. This reliance limits their applicability in scenarios where predicting future anomalies is required, as the ground truth is inherently unavailable. To address this gap, we introduce the concept of Time-Series Anomaly Prediction (TSAP), which focuses on forecasting the occurrence and progression of anomalies in time series simultaneously without relying on ground truth. In this paper, we propose a novel exemplar-based pre-training and fine-tuning pipeline tailored to this task, based on recent achievements in online time-series forecasting techniques. The pipeline begins with an offline pre-training phase, where a deep learning model is trained to capture the underlying temporal correlations in time-series data. During the online fine-tuning stage, a three-step process is employed to predict the timing and evolution of anomalies. This process includes prediction and anomaly detection, motif search for similar patterns, and fine-tuning using exemplars. These steps are repeated as new data arrives. We evaluate the proposed method against state-of-the-art approaches from various relevant categories on both real-world and synthetic datasets. Experimental results show that the proposed method improves anomaly detection accuracy by up to 53.8% in terms of F1 score and enhances time-series forecasting accuracy during and after anomaly periods by up to 82.4% and 49.1% in terms of MSE. Through analysing the results, we prove the proposed method's effectiveness in addressing the new TSAP tasks, which are incapable of being handled by current time-series anomaly detection or online time-series forecasting methods.

The synergistic development of Internet of Things(IoT), robotics, and Artificial Intelligence (AI) is reshaping the technological paradigms of interdisciplinary laboratories and industrial ecosystems. IoT-enabled vertical farming systems demonstrate significant advantages, achieving yield enhancement while reducing carbon emissions compared to traditional agriculture, thereby providing innovative solutions for sustainable food production. The advancement of robotic technologies further expands the application dimensions of mobile intelligent sensors in vertical farm IoT networks. Based on an autonomous farming system that integrates Unmanned Aerial Vehicle (UAV), sensors, and modular vertical farming units, this study proposes a three dimensional Scene Graph (3DSG)-based hierarchical mapping method for the dynamic monitoring of plant and fruit growth. Through feedback mechanisms, the system optimizes growth conditions by adjusting lighting and nutrient delivery, while the hierarchical mapping architecture reduces detection errors and enables comprehensive 3D visualization. The main contributions of this research include: 1) Pioneering application of 3DSG technology to establish a multi-dimensional spatiotemporal representation model for plant growth processes, supporting interpretable analysis and traceable monitoring; 2) Establishing an uncertainty model through error propagation by systematically analyzing sensor models (covering various common sensor combinations) and integrating these models into 3D object pose estimation algorithms. This highlights the necessity of hierarchical abstraction levels. The system is validated through simulations and real-world experiments, providing a quantitative evaluation of object pose estimation; and 3) An IoT-driven intelligent vertical farming architecture that integrating mobile robotic perception networks and environmental regulation devices, enabling dynamic acquisition and closed-loop control of plant growth parameters. Open-source code is available at https://github.com/allenthreee/scene_graph, video link: https://youtu.be/dhc8RLmX7hc.

Deep learning approaches have been widely adopted for precipitation nowcasting in recent years. Previous studies mainly focus on proposing new model architectures to improve pixel-wise metrics. However, they frequently result in blurry predictions which provide limited utility to forecasting operations. In this work, we propose a new Fourier Amplitude and Correlation Loss (FACL) which consists of two novel loss terms: Fourier Amplitude Loss (FAL) and Fourier Correlation Loss (FCL). FAL regularizes the Fourier amplitude of the model prediction and FCL complements the missing phase information. The two loss terms work together to replace the traditional L2 losses such as MSE and weighted MSE for the spatiotemporal prediction problem on signal-based data. Our method is generic, parameter-free and efficient. Extensive experiments using one synthetic dataset and three radar echo datasets demonstrate that our method improves perceptual metrics and meteorology skill scores, with a small trade-off to pixel-wise accuracy and structural similarity. Moreover, to improve the error margin in meteorological skill scores such as Critical Success Index (CSI) and Fractions Skill Score (FSS), we propose and adopt the Regional Histogram Divergence (RHD), a distance metric that considers the patch-wise similarity between signal-based imagery patterns with tolerance to local transforms.