在本文中，我们提出了一种基于短期内存网络的长期方法，以根据过去的测量值预测公共建筑物的能源消耗。我们的方法包括三个主要步骤：数据处理步骤，培训和验证步骤，最后是预测步骤。我们在一个数据集上测试了我们的方法，该数据集由英国国家档案馆的主要建筑物的主要建筑物，在KEW中，作为评估指标，我们使用了平均绝对错误（MAE）和平均绝对百分比错误（Mape）。

本文比较分析随机森林的性能和基于历史数据预测能源消耗的领域的梯度增强算法的性能。应用两种算法以单独预测能源消耗，然后使用加权平均合奏方法合并在一起。所达到的实验结果之间的比较证明，加权平均合奏方法比单独应用的两种算法中的每种都提供了更准确的结果。

We present a novel corpus for French dialect identification comprising 413,522 French text samples collected from public news websites in Belgium, Canada, France and Switzerland. To ensure an accurate estimation of the dialect identification performance of models, we designed the corpus to eliminate potential biases related to topic, writing style, and publication source. More precisely, the training, validation and test splits are collected from different news websites, while searching for different keywords (topics). This leads to a French cross-domain (FreCDo) dialect identification task. We conduct experiments with four competitive baselines, a fine-tuned CamemBERT model, an XGBoost based on fine-tuned CamemBERT features, a Support Vector Machines (SVM) classifier based on fine-tuned CamemBERT features, and an SVM based on word n-grams. Aside from presenting quantitative results, we also make an analysis of the most discriminative features learned by CamemBERT. Our corpus is available at https://github.com/MihaelaGaman/FreCDo.

We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT). In many real-world scenarios, input features are not readily available at test time and must instead be acquired at significant cost. With A2MT, we aim to learn agents that actively select which modalities of an input to acquire, trading off acquisition cost and predictive performance. A2MT extends a previous task called active feature acquisition to temporal decision making about high-dimensional inputs. Further, we propose a method based on the Perceiver IO architecture to address A2MT in practice. Our agents are able to solve a novel synthetic scenario requiring practically relevant cross-modal reasoning skills. On two large-scale, real-world datasets, Kinetics-700 and AudioSet, our agents successfully learn cost-reactive acquisition behavior. However, an ablation reveals they are unable to learn to learn adaptive acquisition strategies, emphasizing the difficulty of the task even for state-of-the-art models. Applications of A2MT may be impactful in domains like medicine, robotics, or finance, where modalities differ in acquisition cost and informativeness.