scholarly journals Research on the trend forecasting model of power communication network operation

2016 ◽  
Author(s):  
Jian Shi ◽  
Hai-yang Wu
Keyword(s):  
Communication Network ◽  
Forecasting Model ◽  
Trend Forecasting ◽  
Network Operation

A novel cryptocurrency price trend forecasting model based on LightGBM

2020 ◽  
Vol 32 ◽  
pp. 101084 ◽  
Author(s):  
Xiaolei Sun ◽  
Mingxi Liu ◽  
Zeqian Sima
Keyword(s):  
Forecasting Model ◽  
Price Trend ◽  
Model Based ◽  
Trend Forecasting

scholarly journals Stock market trend prediction using regression errors

2021 ◽  
Author(s):  
Omair Sandhu
Keyword(s):  
Stock Market ◽  
Factor Model ◽  
Regression Line ◽  
Stock Exchanges ◽  
Forecasting Model ◽  
Trend Prediction ◽  
Monthly Data ◽  
Trend Forecasting ◽  
Direction Of Movement ◽  
Multiple Assets

Stock exchanges are one of the major areas of investment because of the possibility of high returns and big winners. They are affected by a variety of factors making it difficult to get consistent returns and accurate predictions when using systematic forecasting techniques. We consider a portfolio formation problem by comparison of the trend strengths of multiple assets. The trend strength determined by the slope and errors from the regression line provides a useful method for crosssectional comparison of stocks. We use weekly and monthly data from 1965 to 2018 from the CRSP US Stocks Database to test the performance of these factors when used to predict the direction of movement for an asset in the future. We investigate the feasibility of this two factor model and various methods of combination to determine the optimal stock trend forecasting model.

scholarly journals Stock market trend prediction using regression errors

2021 ◽  
Author(s):  
Omair Sandhu
Keyword(s):  
Stock Market ◽  
Factor Model ◽  
Regression Line ◽  
Stock Exchanges ◽  
Forecasting Model ◽  
Trend Prediction ◽  
Monthly Data ◽  
Trend Forecasting ◽  
Direction Of Movement ◽  
Multiple Assets

Stock exchanges are one of the major areas of investment because of the possibility of high returns and big winners. They are affected by a variety of factors making it difficult to get consistent returns and accurate predictions when using systematic forecasting techniques. We consider a portfolio formation problem by comparison of the trend strengths of multiple assets. The trend strength determined by the slope and errors from the regression line provides a useful method for crosssectional comparison of stocks. We use weekly and monthly data from 1965 to 2018 from the CRSP US Stocks Database to test the performance of these factors when used to predict the direction of movement for an asset in the future. We investigate the feasibility of this two factor model and various methods of combination to determine the optimal stock trend forecasting model.

Forecasting Russian Stock Market Trends

2003 ◽  
pp. 81-94 ◽  
Author(s):  
A. Rozhkov
Keyword(s):  
Stock Market ◽  
Economic Indicators ◽  
Forecasting Model ◽  
Reverse Trend ◽  
Trend Forecasting ◽  
Market Trends ◽  
Russian Stock Market

The article is devoted to investigating methods for forecasting long-term Russian stock market trends. The purpose of research is creation of the forecasting model capable of forming a reverse trend signal in the stock market. The index of trend forecasting constructed in the article includes different economic indicators and thus has high forecasting ability.

scholarly journals Medium—And Long-Term Load Forecasting Method for Group Objects Based on the Image Representation Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Daolu Zhang ◽  
Weiling Guan ◽  
Jiajun Yang ◽  
Huang Yu ◽  
WenCong Xiao ◽  
...  
Keyword(s):  
Distribution Network ◽  
Image Representation ◽  
Load Forecasting ◽  
Representation Learning ◽  
Forecasting Model ◽  
Distribution Transformer ◽  
Change Rate ◽  
Forecasting Method ◽  
Trend Forecasting

Medium-and long-term load forecasting in the distribution network has important guiding significance for overload warning of distribution transformer, transformation of distribution network and other scenarios. However, there are many constraints in the forecasting process. For example, there are many predict objects, the data sample size of a single predict object is small, and the long term load trend is not obvious. The forecasting method based on neural network is difficult to model due to lack of data, and the forecasting method based on time sequence law commonly used in engineering is highly subjective, which is not effective. Aiming at the above problems, this paper takes distribution transformer as the research object and proposes a medium-and long-term load forecasting method for group objects based on Image Representation Learning (IRL). Firstly, the data of distribution transformer is preprocessed in order to restore the load variation in natural state. And then, the load forecasting process is decoupled into two parts: the load trend forecasting of the next year and numerical forecasting of the load change rate. Secondly, the load images covering annual and inter-annual data change information are constructed. Meanwhile, an Image Representation Learning forecasting model based on convolutional neural network, which will use to predict the load development trend, is obtained by using load images for training; And according to the data shape, the group classification of the data in different periods are carried out to train the corresponding group objects forecasting model of each group. Based on the forecasting data and the load trend forecasting result, the group forecasting model corresponding to the forecasting data can be selected to realize the numerical forecasting of load change rate. Due to the large number of predict objects, this paper introduces the evaluation index of group forecasting to measure the forecasting effect of different methods. Finally, the experimental results show that, compared with the existing distribution transformer forecasting methods, the method proposed in this paper has a better overall forecasting effect, and provides a new idea and solution for the medium-and long-term intelligent load forecasting of the distribution network.

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