scholarly journals NEURAL NETWORK ALGORITHM FOR CHOOSING METHODS OF TIME SERIES FORECASTING

2019 ◽  
pp. 51-60 ◽  
Author(s):  
Yuri Vladimirovich Dubenko ◽  
Evgeny Evgenyevich Dyshkant
Keyword(s):  
Time Series ◽  
Negative Impact ◽  
Time Estimation ◽  
Optimality Criteria ◽  
Prediction Methods ◽  
Forecast Horizon ◽  
General Estimate ◽  
Forecasting Method ◽  
Neural Network Algorithm ◽  
Wide Group

The prediction unit is one of the most important components of intelligent control systems. The results of its operation influence the type of control actions generated by the system. The performance of the unit depends on the prediction methods used. The accuracy of the result of the prediction block depends on the choice of the forecasting method. Thus, uncertainty when choosing a forecasting method is a factor that has a negative impact on the reliability of the result of the prediction block and, as consequence, on the reliability of the control system as a whole. The analysis results of the work in this field suggest that the problem of choosing the best forecasting methods has been worked out mainly at the conceptual level. The disadvantages of the considered works are the lack of specifying the mechanism for implementing the proposed algorithms, as well as the potential result of their work is a wide group of prediction methods that are found optimal. In one of the considered works, the expert system is indicated as a mechanism for solving the problem, and the algorithm for modifying and updating the rules is not specified. We have proposed the algorithm based on the use of a precedent analysis method realized in artificial neural networks, which allows to solve these problems. The statistical indicators of the time series, as well as the forecast horizon are used as characteristics of the object and the forecasting task, which constitute the set of attributes of a precedent. A set of solutions to the problem are the applied prediction methods. The set of results is a general estimate of the solution calculated on the basis of the values of the optimality criteria. At the same time, estimation of the optimality of the forecasting method is performed on the basis of the criteria of accuracy and speed, which are based on the prediction error, as well as the length of time spent on obtaining the forecast. The effectiveness of the proposed algorithm has been proved by the results of the experiment.

scholarly journals Error Evolution Patterns in Multi-Step Ahead Streamflow Forecasting

10.29007/84k6 ◽  
2018 ◽  
Author(s):  
Georgia Papacharalampous ◽  
Hristos Tyralis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Time Series ◽  
Single Case ◽  
Practical Interest ◽  
Comparative Case Study ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Time Step ◽  
Forecasting Methods ◽  
Forecasting Method

Multi-step ahead streamflow forecasting is of practical interest. We examine the error evolution in multi-step ahead forecasting by conducting six simulation experiments. Within each of these experiments we compare the error evolution patterns created by 16 forecasting methods, when the latter are applied to 2 000 time series. Our findings suggest that the error evolution can differ significantly from the one forecasting method to the other and that some forecasting methods are more useful than others. However, the errors computed at each time step of a forecast horizon for a specific single-case study strongly depend on the case examined and can be either small or large, regardless of the used forecasting method and the time step of interest. This fact is illustrated with a comparative case study using 92 monthly time series of streamflow.

Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches

2019 ◽  
Vol 15 (2) ◽  
pp. 647-659 ◽  
Author(s):  
Zahra Moeini Najafabadi ◽  
Mehdi Bijari ◽  
Mehdi Khashei
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Stock Markets ◽  
Time Series Prediction ◽  
Investment Decisions ◽  
Rate Of Return ◽  
Expected Returns ◽  
Prediction Methods ◽  
Content Type ◽  
Markowitz Model

Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

scholarly journals Forecasting method for grouped time series with the use of k-means algorithm

2015 ◽  
Vol 9 ◽  
pp. 4813-4830 ◽  
Author(s):  
Nadezhda N. Astakhova ◽  
Liliya A. Demidova ◽  
Evgeny V. Nikulchev
Keyword(s):  
Time Series ◽  
Forecasting Method
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