# 统计代写|时间序列分析代写Time-Series Analysis代考|STAT510

## 统计代写|时间序列分析代写Time-Series Analysis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 form, and only a quantitative analysis was performed in this work, therefore leaving 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.

## 统计代写|时间序列分析代写Time-Series Analysis代考|ARFIMA and ARTFIMA Processes in Time Series with Applications

The Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models, introduced in the classic book by Box and Jenkins (1976), are able to capture short-range dependence. The dependence between time series observations of ARMA models decreases rapidly as the time lag increases. However, many economic time series depict long-range dependence, also called long-memory or long-range persistence. The autocorrelation function of such ARMA processes decays exponentially, and these processes fail to model the longmemory phenomenon present in a time series. The integrated processes have infinite lag memory, and the process reduces to a stationary process with short-range dependence after a finite number of differences. Obviously, the order of differencing for these processes is an integer.

The interesting feature of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process is that its autocorrelation functions decay much slower than exponential decay, which makes it a strong candidate for modeling a time series having a long memory. For time series possessing long memory, these ARFIMA processes provide an improved fit and better predictions in comparison to ARMA processes.

Let $\left{Z_t, \mathrm{t}=1, \ldots, \mathrm{n}\right}$ be a realization of a time series with sample mean $\bar{Z}$ and variance $S^2$. The adjusted partial sums are defined by
$$S_t=\sum_{j=1}^t Z_t-t \bar{Z}, \quad \mathrm{t}=1,2, \ldots, \mathrm{n}$$
and the rescaled adjusted range by
$$\mathrm{R}=S^{-1}\left{\max \left(S_1, S_2, \ldots, S_n\right)-\min \left(S_1, S_2, \ldots, S_n\right)\right}$$

If random variables $\left{Z_j, j=1,2, \ldots\right}$ are identically and independently distributed, one can easily verify that $\mathrm{R}$ is proportional to $n^H$ with $\mathrm{H}=0.5$. However, while analyzing river flow time series, Hurst (1951) found the value of $\mathrm{H}$ to be around $0.73$. The coefficient $\mathrm{R}$ is known as the “Hurst exponent” or “Hurst coefficient”, and this phenomenon is known as the Hurst phenomenon, first observed by Hurst $(1951,1956)$ in geophysical time series. The observed value of $\mathrm{H}$ (0.73) led to the conclusion that it has been caused by long-range dependence or persistence in series.

# 时间序列分析代考

## 统计代写|时间序列分析代写Time-Series Analysis代考|STATE-OF-THE-ART FORECASTING

He (2017) 提出了一种用于短期负荷预测 (STLF) 的深度学习方法。作者使用卷积神经网络 (CNN) 学习丰富的特征，使用 RNN 学习历史负载序列动态。CNN 非常适用于图像分类应用，但它们不适用于学习序列中的时间行为。但是，假设 CNN 和 RNN 结合使用。在这种情况下，他们可以改进表示的学习，从而提高预测的准确性。焦等。(2018) 提出了一种基于 LSTM 的 STLF 方法，用于使用多个相关序列的非住宅负荷。首先使用 k-means 算法对多个序列进行分类，以确定负载消耗行为。Spearman 相关性用于查找不同序列的时间相关性。该方法对于规则模式数据表现非常好，但 LSTM 的性能对于不规则数据会急剧下降。在这种情况下添加更多特征也降低了预测准确性。

## 统计代写|时间序列分析代写Time-Series Analysis代考|ARFIMA and ARTFIMA Processes in Time Series with Applications

Box 和 Jenkins（1976 年）在经典著作中介绍的自回归移动平均 (ARMA) 和自回归综合移动平均 (ARIMA) 模型能够捕获短程依赖性。随着时间滞后的增加，ARMA 模型的时间序列观测值之间的依赖性迅速降低。然而，许多经济时间序列描述了长期依赖性，也称为长期记忆或长期持久性。这种 ARMA 过程的自相关函数呈指数衰减，并且这些过程无法模拟时间序列中存在的长记忆现象。积分过程具有无限滞后记忆，经过有限次差分后，过程退化为具有短程依赖的平稳过程。显然，这些过程的差分顺序是一个整数。

\mathrm{R}=S^{-1}\left{\max \left(S_1, S_2, \ldots, S_n\right)-\min \left(S_1, S_2, \ldots, S_n\right)\right }\mathrm{R}=S^{-1}\left{\max \left(S_1, S_2, \ldots, S_n\right)-\min \left(S_1, S_2, \ldots, S_n\right)\right }

myassignments-help数学代考价格说明

1、客户需提供物理代考的网址，相关账户，以及课程名称，Textbook等相关资料~客服会根据作业数量和持续时间给您定价~使收费透明，让您清楚的知道您的钱花在什么地方。

2、数学代写一般每篇报价约为600—1000rmb，费用根据持续时间、周作业量、成绩要求有所浮动(持续时间越长约便宜、周作业量越多约贵、成绩要求越高越贵)，报价后价格觉得合适，可以先付一周的款，我们帮你试做，满意后再继续，遇到Fail全额退款。

3、myassignments-help公司所有MATH作业代写服务支持付半款，全款，周付款，周付款一方面方便大家查阅自己的分数，一方面也方便大家资金周转，注意:每周固定周一时先预付下周的定金，不付定金不予继续做。物理代写一次性付清打9.5折。

Math作业代写、数学代写常见问题

myassignments-help擅长领域包含但不是全部: