scholarly journals LEARNING A COMBINED MODEL OF TIME SERIES FORECASTING

2021 ◽  
Vol 3 (1) ◽  
pp. 44-48
Author(s):  
F. E. Geche ◽  
◽  
O. Yu. Mulesa ◽  
A. Ye. Batyuk ◽  
V. Yu. Smolanka ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Weighting Factor ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Basic Model ◽  
Time Period ◽  
Optimal Value ◽  
Predicted Values ◽  
Combined Forecasting

The method of construction of the combined model of forecast ing of time series based on basic models of forecasting is developed in the work. The set of basic models is dynamic, ie new prediction models can be included in this set. Models also can be deleted depending on the properties of the time series. For the synthesis of a combined model of forecasting time series with a given forecast step, the optimal step of prehistory is determined at the beginning. Next the functional is constructed. The optimal prehistory step is determined using the autoregression method for a fixed forecast step. It determines the period of time at which the accuracy of models from the base set is analyzed. For each basic model during the process of the construction of the combined model is determined by the weighting factor with which it will be included in the combined model. The weights of the basic models are determined based on their forecasting accuracy for the time period determined by the prehistory step. The weights reflect the degree of influence of the base models on the accuracy of the combined model forecasting. After construction of the combined model, its training is carried out and those basic models which will be included in the final combined model of forecasting are defined. The rule of inclusion of basic models in the combined model is established. While including basic models in the combined forecasting model, their weights are taken into account, which depends on the same parameter. The optimal value of the parameter is determined by minimizing the given functional, which sets the standard deviation between the actual and predicted values ​​of the time series. Weights with optimal parameters are ranked in decreasing order and are used to include basic models in the combined model. As a result of this approach, as predicted values for the real time series show, it was possible to significantly improve the forecasting accuracy of the combined model in many cases. The developed method of training provides the flexibility of the combined model and its application to a wide class of time series. The results obtained in this work contribute to solving the problem of choosing the most effective basic models by synthesizing them into one combined model.

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.

scholarly journals Power Customer Complaint Prediction Model Based on Time Series Analysis

2020 ◽  
Vol 34 (4) ◽  
pp. 471-477
Author(s):  
Shuangshuang Guo ◽  
Linlin Tang ◽  
Xiaoyan Guo ◽  
Zheng Huang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Customer Service ◽  
Prediction Models ◽  
Moving Average ◽  
Customer Complaints ◽  
Customer Complaint ◽  
Optimized Model ◽  
Predicted Values ◽  
Bpnn Model

To improve customer service of power enterprises, this paper constructs an intelligent prediction model for customer complaints in the near future based on the big data on power service. Firstly, three customer complaint prediction models were established, separately based on autoregressive integrated moving average (ARIMA) time series algorithm, multiple linear regression (MLR) algorithm, and backpropagation neural network (BPNN) algorithm. The predicted values of the three models were compared with the real values. Through the comparison, the BPNN model was found to achieve the best predictive effect. To help the BPNN avoid local minimum, the genetic algorithm (GA) was introduced to optimize the BPNN model. Finally, several experiments were conducted to verify the effect of the optimized model. The results show that the relative error of the optimized model was less than 40% in most cases. The proposed model can greatly improve the customer service of power enterprises.

scholarly journals Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 960
Author(s):  
Peng Jiang ◽  
Yi-Chung Hu ◽  
Wenbao Wang ◽  
Hang Jiang ◽  
Geng Wu
Keyword(s):  
Regression Analysis ◽  
Energy Demand ◽  
Prediction Models ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Series Data ◽  
Grey Prediction ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Energy Demand Forecasting

Time series data for decision problems such as energy demand forecasting are often derived from uncertain assessments, and do not meet any statistical assumptions. The interval grey number becomes an appropriate representation for an uncertain and imprecise observation. In order to obtain nonlinear interval grey numbers with better forecasting accuracy, this study proposes a combined model by fusing interval grey numbers estimated by neural networks (NNs) and the grey prediction models. The proposed model first uses interval regression analysis using NNs to estimate interval grey numbers for a real valued sequence; and then a grey residual modification model is constructed using the upper and lower wrapping sequences obtained by NNs. It turns out that two different kinds of interval grey numbers can be estimated by nonlinear interval regression analysis. Forecasting accuracy on real data sequences was then examined by the best non-fuzzy performance values of the combined model. The proposed combined model performed well compared with the other interval grey prediction models considered.

Wind Power Prediction Model Based on the Combination of Gray Theory and Time Series Forecasting Methods

2013 ◽  
Vol 448-453 ◽  
pp. 1721-1726
Author(s):  
Xiao Juan Han ◽  
Xi Lin Zhang ◽  
Yue Yan Chen ◽  
Fang Yuan Meng
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Wind Farm ◽  
Prediction Models ◽  
Forecast Model ◽  
Time Series Forecasting ◽  
Power Prediction ◽  
Combined Model ◽  
Gray Theory ◽  
The Individual

Accurate wind power predicting is helpful for the dispatch and safety operation of grid, so as to increase wind power penetration, Two representative prediction models, based on gray theory and time series forecasting method respectively, were selected, the farm measured data were input to the models and their own prediction results were obtained respectively. Finally, the prediction results of the combined model were compared with the two individual models, verifying the feasibility of the combined model to wind farm generation capacity forecast. It is concluded that the combined forecast model can predict more accurately than the individual forecast model.

scholarly journals Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wu Xiang ◽  
Qian Jian-sheng ◽  
Huang Cheng-hua ◽  
Zhang Li
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Prediction Models ◽  
Forecasting Model ◽  
Gas Concentration ◽  
Concentration Time ◽  
Proposed Model ◽  
Concentration Time Series ◽  
Predicted Values ◽  
Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction.

scholarly journals A new spectral conjugate gradient method and ARIMA combined forecasting model and application

2018 ◽  
Vol 12 (3) ◽  
pp. 245-252 ◽  
Author(s):  
Rui Shan ◽  
Guofang Wang ◽  
Wei Huang ◽  
Jingyi Zhao ◽  
Wen Liu
Keyword(s):  
Time Series ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Moving Average ◽  
Practical Significance ◽  
Combined Model ◽  
Actual Time ◽  
Spectral Conjugate Gradient ◽  
Combined Forecasting

It is of great practical significance to fit and predict actual time series. Based on the theories of time series analysis and unconstrained optimization, a new spectral conjugate gradient method–autoregressive integrated moving average combined model (FHS spectral CG–ARIMA combined model) is proposed to fit and predict the actual time series. First, combining the characteristics and advantages of different CG methods, we propose Fang–Hestenes–Stiefel algorithm (FHS). FHS satisfies the automatic descent property and has global convergence under the reasonable assumptions and Wolfe search. Second, many numerical results have been given there: compared with other related algorithms, FHS algorithm has obvious advantages. Third, FHS spectral CG–ARIMA combined model is given in detail. Fourth, the combined model is applied to fit the actual time series and the fitting effect is found to be remarkable.

Dilution studies on three marine outfalls in South Africa

1995 ◽  
Vol 32 (2) ◽  
pp. 297-304
Author(s):  
Willem A. M. Botes ◽  
J. F. Kapp
Keyword(s):  
South Africa ◽  
South African ◽  
Field Data ◽  
Prediction Models ◽  
Feasibility Studies ◽  
The South ◽  
Average Current ◽  
Predicted Values ◽  
Prediction Techniques ◽  
Domestic Effluent

Field dilution studies were conducted on three “deep” water marine outfalls located along the South African coast to establish the comparibility of actual achievable initial dilutions against the theoretical predicted values and, where appropriate, to make recommendations regarding the applicability of the different prediction techniques in the design of future outfalls. The physical processes along the 3000 km long coastline of South Africa are diverse, ranging from dynamic sub-tropical waters on the east coast to cold, stratified stagnant conditions on the west coast. Fourteen existing offshore marine outfalls serve medium to large industries and various local authorities (domestic effluent). For this investigation three outfalls were selected to represent the range of outfall types as well as the diversity of the physical conditions of the South African coastline. The predicted dilutions, using various approaches, compared well with the measured dilutions. It was found that the application of more “simple” prediction techniques (using average current velocities and ambient densities) may be more practical, ensuring a conservative approach, in pre-feasibility studies, compared to the more detailed prediction models, which uses accurate field data (stratification and current profiles), when extensive field data is not readily available.

scholarly journals Complexity traits and synchrony of cryptocurrencies price dynamics

2021 ◽  
Author(s):  
Davide Provenzano ◽  
Rodolfo Baggio
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Complex Structures ◽  
Slope Parameter ◽  
Exponential Distributions ◽  
Horizontal Visibility ◽  
Time Period ◽  
Before And After ◽  
Visibility Algorithm ◽  
Similar Complex

AbstractIn this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.

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.

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