scholarly journals Time Series Smoothing Improving Forecasting

2021 ◽  
Vol 26 (1) ◽  
pp. 60-70
Author(s):  
Vadim Romanuke
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Models ◽  
Forecasting Accuracy ◽  
Decay Properties ◽  
Network Methods ◽  
Optimal Values ◽  
Open Question

Abstract Both statistical and neural network methods may fail in forecasting time series even operating on a great amount of data. It is an open question of which amount fits best to make sufficiently accurate forecasts on it. This implies that the length or time series might be optimised. Hence, the objective is to improve the quality of forecasting by an assumption that parameters are set nearly at their optimal values. To achieve objective, the two types of the benchmark time series are considered: sine-shaped series and random-like series with repeatability. Trend, seasonality, and decay properties embedded into each type. Based on the benchmark of 24 time series models, it is ascertained that, for improving the forecasting, the time series should be smoothed and then downsampled. These operations can be fulfilled successively until the improvement fails. If preliminary smoothing worsens forecasts, the raw time series is straightforwardly downsampled until the forecasting accuracy starts dropping. However, if time series has a visible property of being noised, the preliminary smoothing is strongly recommended.

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

Prospects for the implementation of neural network technologies in radar information processing systems

2021 ◽  
Author(s):  
E.V. Egorova ◽  
A.N. Rybakov ◽  
M.H. Aksyaitov
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Network Technology ◽  
Network Methods ◽  
External Conditions ◽  
Network Technologies ◽  
Radar Information ◽  
Neural Network Technologies ◽  
Neural Network Technology

Conducted studies of the phased implementation of neural network technologies in the practice of processing radar information, providing for a gradual increase in the level of neural network methods in processing systems, have shown that the use of neural network technologies can improve the quality of radar information processing in the most difficult conditions that require high computing power, when the dynamics of changes in external conditions is very is high and traditional approaches to the creation of processing systems are not able to provide the required level of efficiency. The need to develop theoretical provisions for neural network processing of radar information was revealed, while the main features of information processing in radars determine the relevance of research devoted to preventing the reduction in the quality of radar images in conditions of a large number of targets and a complex «jamming» environment based on the rational use of neural network technology. Analysis of the phased implementation of neural network technologies in radar information processing systems, as well as the use of neural network technology for processing radar information in terms of search and research, makes it possible to increase the efficiency of neural network methods for all processing tasks. Assessment of the required performance of computational tools allows us to single out the main neural network paradigms, the use of which gives a tangible increase in the efficiency of radar information processing, such as multilayer perceptron, Hopfield associative memory and self-organizing Kohonen network, while it is possible to rank the proposed methods in accordance with the required performance, undemanding to computing power and implemented on existing or promising computing facilities with software implementation of neural network paradigms. The analysis of possible directions for improving the quality of radar information processing does not claim to fully cover the entire multifaceted area of such studies. In this paper, only the most universal and widespread neural network paradigms are considered and the main part of possible areas of their application is analyzed. However, the proposed options show that the use of neural network technologies in critical tasks will improve the efficiency of radar information processing for complex, rapidly changing external conditions. The use of the principles of self-learning and the developed apparatus for the synthesis of neural network methods will reduce the duration and complexity of theoretical research, the conduct of which is a necessary and mandatory part of the traditional approach. In the course of further research, some of the proposed methods can be refined, as well as the emergence of new methods that make it possible to more fully use the advantages of neural network technology. Carrying out further research work in these areas will give a powerful stimulating impetus for the creation in the future of highly efficient methods for processing radar information, which can be implemented on the available element base.

Roughness/error trade-offs in neural network time series models

1997 ◽  
Author(s):  
Steven C. Gustafson ◽  
Gordon R. Little ◽  
John S. Loomis ◽  
Theresa A. Tuthill
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Models ◽  
Trade Offs ◽  
Network Time

scholarly journals Storm-surge prediction at the Tanshui estuary: development model for maximum storm surges

2013 ◽  
Vol 1 (6) ◽  
pp. 7333-7356 ◽  
Author(s):  
C.-P. Tsai ◽  
C.-Y. You ◽  
C.-Y. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Storm Surges ◽  
Development Model ◽  
Series Data ◽  
Multilayer Perception ◽  
Optimum Parameters ◽  
Network Methods ◽  
Upstream Station

Abstract. This study applies artificial networks, including both the supervised multilayer perception neural network and the radial basis function neural network to the prediction of storm-surges at the Tanshui estuary in Taiwan. The optimum parameters for the prediction of the maximum storm-surges based on 22 previous sets of data are discussed. Two different neural network methods are adopted to build models for the prediction of storm surges and the importance of each factor is also discussed. The factors relevant to the maximum storm surges, including the pressure difference, maximum wind speed and wind direction at the Tanshui Estuary and the flow rate at the upstream station, are all investigated. These good results can further be applied to build a neural network model for prediction of storm surges with time series data.

scholarly journals Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks

2020 ◽  
Vol 6 (2) ◽  
pp. 137-148
Author(s):  
J. Oliver Muncharaz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Stock Market ◽  
Recurrent Neural Network ◽  
Stock Prices ◽  
Arima Model ◽  
Time Series Models ◽  
Prediction Errors ◽  
The Creation

In the financial literature, there is great interest in the prediction of stock prices. Stock prediction is necessary for the creation of different investment strategies, both speculative and hedging ones. The application of neural networks has involved a change in the creation of predictive models. In this paper, we analyze the capacity of recurrent neural networks, in particular the long short-term recurrent neural network (LSTM) as opposed to classic time series models such as the Exponential Smooth Time Series (ETS) and the Arima model (ARIMA). These models have been estimated for 284 stocks from the S&P 500 stock market index, comparing the MAE obtained from their predictions. The results obtained confirm a significant reduction in prediction errors when LSTM is applied. These results are consistent with other similar studies applied to stocks included in other stock market indices, as well as other financial assets such as exchange rates.

Sign in / Sign up

Export Citation Format

Share Document