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# 统计代写|时间序列代写Time Series代考|STAT40860 STATE-OF-THE-ART FORECASTING

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## 统计代写|时间序列代写Time Series代考|STATE-OF-THE-ART FORECASTING

He (2017) proposed a DL approach for short-term load forecasting (STLF). The author used a convolutional neural network (CNN) to learn rich features and RNN to learn the historical load sequence dynamics. CNNs are well suited for image classification applications, but they are not useful for learning temporal behavior in a sequence. However, suppose CNN and RNN are used in conjunction. In that case, they can improve learning of representations, thus improving forecasting accuracy.
Jiao et al. (2018) proposed a method for STLF based on LSTM for nonresidential loads using multiple correlated sequences. The multiple sequences are first classified using the k-means algorithm to determine the load consumption behavior. Spearman’s correlation is used to find the time dependencies for different series. The method performed very well for regular patterned data, but LSTM’s performance degraded drastically for irregular data. The addition of more features in this case also reduced the forecasting accuracy.

Residential load series possess a different challenge versus utility level or aggregated load. Each household can have uncertain load consumption behavior, making it highly volatile and very difficult to predict. Shi et al. (2018) proposed a two-staged method for residential load forecasting. The first stage pooled the individual household data, thus increasing the data volume and preventing overfitting for a small number of layers in the network. The second stage used the pooled data as input to a deep RNN for forecasting purposes. The model used was very naïve in a margin for improvement in model architecture.

Kong et al. (2019) addressed the above problem and performed a density-based clustering of the household knowns with Density-Based Spatial Clustering of Application with Noise (DBSCAN) (Ester et al., 1996). DBSCAN does not require the number of clusters to initialize, and it also has the notion of outliers. Then, an LSTM-based framework was adopted to forecast. The accuracy at the individual-level forecast was low; however, the forecast yield was much better after aggregation.

## 统计代写|时间序列代写Time Series代考|DEEP LEARNING METHODS

Deep neural networks consist of a large number of hidden layers and neurons. This large number allows the network to learn the non-linearities present in the input, making it impossible for a shallow network to learn. This section presents DL techniques specially tailored for learning temporal data or sequences. The two standard architectures for this purpose are RNNs and LSTMs.

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。
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