scholarly journals One-step ahead prediction of <i>fo</i>F2 using time series forecasting techniques

2005 ◽  
Vol 23 (9) ◽  
pp. 3035-3042 ◽  
Author(s):  
K. Koutroumbas ◽  
A. Belehaki
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Nearest Neighbor ◽  
Middle Latitude ◽  
Performance Criterion ◽  
Time Series Forecasting ◽  
K Nearest Neighbor ◽  
Linear Predictor ◽  
The Neural Network ◽  
One Step

Abstract. In this paper the problem of one-step ahead prediction of the critical frequency (foF2) of the middle-latitude ionosphere, using time series forecasting methods, is considered. The whole study is based on a sample of about 58000 observations of foF2 with 15-min time resolution, derived from the Athens digisonde ionograms taken from the Digisonde Portable Sounder (DPS4) located at Palaia Penteli (38° N, 23.5° E), for the period from October 2002 to May 2004. First, the embedding dimension of the dynamical system that generates the above sample is estimated using the false nearest neighbor method. This information is then utilized for the training of the predictors employed in this study, which are the linear predictor, the neural network predictor, the persistence predictor and the k-nearest neighbor predictor. The results obtained by the above predictors suggest that, as far as the mean square error is considered as performance criterion, the first two predictors are significantly better than the latter two predictors. In addition, the results obtained by the linear and the neural network predictors are not significantly different from each other. This may be taken as an indication that a linear model suffices for one step ahead prediction of foF2.

A methodology for applying k-nearest neighbor to time series forecasting

2017 ◽  
Vol 52 (3) ◽  
pp. 2019-2037 ◽  
Author(s):  
Francisco Martínez ◽  
María Pilar Frías ◽  
María Dolores Pérez ◽  
Antonio Jesús Rivera
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Time Series Forecasting ◽  
K Nearest Neighbor

STATISTICAL AND MACHINE LEARNING HIGH-FREQUENCY TIME SERIES FORECASTING METHODS IN AUTOMATIC MODE

2020 ◽  
pp. 3-11
Author(s):  
Sai Van Cuong ◽  
M. V. Shcherbakov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
High Frequency ◽  
Network Models ◽  
Time Series Forecasting ◽  
Neural Network Models ◽  
Automatic Mode ◽  
Forecasting Methods ◽  
The Neural Network

The research of the problem of automatic high-frequency time series forecasting (without expert) is devoted. The efficiency of high-frequency time series forecasting using different statistical and machine learning modelsis investigated. Theclassical statistical forecasting methods are compared with neural network models based on 1000 synthetic sets of high-frequency data. The neural network models give better prediction results, however, it takes more time to compute compared to statistical approaches.

Machine Learning Classification of Tree Cover Type and Application to Forest Management

2018 ◽  
Vol 9 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Duncan MacMichael ◽  
Dong Si
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Forest Management ◽  
Network Model ◽  
Neural Network Model ◽  
Nearest Neighbor ◽  
Tree Cover ◽  
K Nearest Neighbor ◽  
Cover Type ◽  
The Neural Network

This article is driven by three goals. The first is to use machine learning to predict tree cover types, helping to address current challenges faced by U.S. forest management agencies. The second is to bring previous research in the area up-to-date, owing to a lack of development over time. The third is to improve on previous results with new data analysis, higher accuracy, and higher reliability. A Deep Neural Network (DNN) was constructed and compared with three baseline traditional machine learning models: Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN). The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.

Machine Learning Classification of Tree Cover Type and Application to Forest Management

2020 ◽  
pp. 1141-1164
Author(s):  
Duncan MacMichael ◽  
Dong Si
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Forest Management ◽  
Network Model ◽  
Neural Network Model ◽  
Nearest Neighbor ◽  
Tree Cover ◽  
K Nearest Neighbor ◽  
Cover Type ◽  
The Neural Network

This article is driven by three goals. The first is to use machine learning to predict tree cover types, helping to address current challenges faced by U.S. forest management agencies. The second is to bring previous research in the area up-to-date, owing to a lack of development over time. The third is to improve on previous results with new data analysis, higher accuracy, and higher reliability. A Deep Neural Network (DNN) was constructed and compared with three baseline traditional machine learning models: Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN). The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.

A Combined ARIMA and Neural Network Approach for Time Series Forecasting

2011 ◽  
pp. 213-225 ◽  
Author(s):  
G. Peter Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Neural Network Model ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Network Approach ◽  
Neural Network Approach ◽  
Individual Model ◽  
The Neural Network ◽  
Forecasting Performance

This chapter presents a combined ARIMA and neural network approach for time series forecasting. The model contains three steps: (1) fitting a linear ARIMA model to the time series under study, (2) building a neural network model based on the residuals from the ARIMA model, and (3) combine the ARIMA prediction and the neural network result to form the final forecast. By combining different models, we aim to take advantage of the unique modeling capability of each individual model and improve forecasting performance dramatically. The effectiveness of the combining approach is demonstrated and discussed with three applications.

Time Series Forecasting via a Higher Order Neural Network trained with the Extended Kalman Filter for Smart Grid Applications

2013 ◽  
pp. 254-274 ◽  
Author(s):  
Luis J. Ricalde ◽  
Glendy A. Catzin ◽  
Alma Y. Alanis ◽  
Edgar N. Sanchez
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Smart Grids ◽  
Real Data ◽  
Higher Order ◽  
Time Series Forecasting ◽  
Wind Energy Generation ◽  
The Neural Network

This chapter presents the design of a neural network that combines higher order terms in its input layer and an Extended Kalman Filter (EKF)-based algorithm for its training. The neural network-based scheme is defined as a Higher Order Neural Network (HONN), and its applicability is illustrated by means of time series forecasting for three important variables present in smart grids: Electric Load Demand (ELD), Wind Speed (WS), and Wind Energy Generation (WEG). The proposed model is trained and tested using real data values taken from a microgrid system in the UADY School of Engineering. The length of the regression vector is determined via the Lipschitz quotients methodology.

Study on Neural Networks in Parallel Computing Environments

2012 ◽  
Vol 198-199 ◽  
pp. 707-710
Author(s):  
Yu Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Parallel Computing ◽  
Time Series Forecasting ◽  
Optimal Solutions ◽  
Network Function ◽  
The Neural Network ◽  
Computing Environments ◽  
Function Approximator

Neurons are highly interconnected with each other and are communicating via sending and receiving electrochemical signals, thus composing sophisticated network of interconnected and communicating neurons. This paper discuss the structure of the neural network function approximator and the time series forecasting with neural network, the results could help us to obtain the optimal solutions to higher complexity of the problem.

scholarly journals Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting

2019 ◽  
pp. 60-68
Author(s):  
S. V. Sholtanyuk
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Comparative Analysis ◽  
Regression Models ◽  
Time Series Forecasting ◽  
Neural Nets ◽  
Forecasting Model ◽  
The Neural Network ◽  
Accuracy And Stability ◽  
Selection Of

Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed. Comparative analysis of received neural networking forecasting model with linear regression has been performed. Conditions, affecting on accuracy and stability of results of the neural network, have been revealed.

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