Methods for Multi-Step Time Series Forecasting Neural Networks

2011 ◽  
pp. 226-250 ◽  
Author(s):  
Douglas M. Kline
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Iterative Method ◽  
Time Series Forecasting ◽  
Independent Method ◽  
Data Series ◽  
Operating Characteristics ◽  
Quarterly Data ◽  
Joint Method

In this study, we examine two methods for Multi-Step forecasting with neural networks: the Joint Method and the Independent Method. A subset of the M-3 Competition quarterly data series is used for the study. The methods are compared to each other, to a neural network Iterative Method, and to a baseline de-trended de-seasonalized naïve forecast. The operating characteristics of the three methods are also examined. Our findings suggest that for longer forecast horizons the Joint Method performs better, while for short forecast horizons the Independent Method performs better. In addition, the Independent Method always performed at least as well as or better than the baseline naïve and neural network Iterative Methods.

scholarly journals Total ozone time series analysis: a neural network model approach

2004 ◽  
Vol 11 (5/6) ◽  
pp. 683-689 ◽  
Author(s):  
B. M. Monge Sanz ◽  
N. J. Medrano Marqués
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Total Ozone ◽  
Missing Values ◽  
Data Series ◽  
Promising Tool ◽  
Classical Statistics ◽  
Non Linear ◽  
Better Than

Abstract. This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the considered European mid-latitudes. Artificial neural networks (ANNs) are intrinsically non-linear systems, hence they are expected to cope with TO series better than classical statistics do. Moreover, neural networks do not assume the stationarity of the data series so they are also able to follow time-changing situations among the implicated variables. These two features turn NNs into a promising tool to catch the interactions between atmospheric variables, and therefore to extract as much information as possible from the available data in order to make, for example, time series reconstructions or future predictions. Models based on NNs have also proved to be very suitable for the treatment of missing values within the data series. In this paper we present several models based on neural networks to fill the missing periods of data within a total ozone time series, and models able to reconstruct the data series. The results released by the ANNs have been compared with those obtained by using classical statistics methods, and better accuracy has been achieved with the non linear ANNs techniques. Different network structures and training strategies have been tested depending on the specific task to be accomplished.

scholarly journals Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 305 ◽  
Author(s):  
Yepeng Cheng ◽  
Zuren Liu ◽  
Yasuhiko Morimoto
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Short Term Memory ◽  
Sequence Data ◽  
Time Series Forecasting ◽  
Convolutional Networks ◽  
Computation Efficiency ◽  
Temporal Features ◽  
Conditional Time

Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causal convolutional neural network (DC-CNN) and Long-short term memory recurrent neural network (LSTM-RNN), to learn multi-range and multi-level features from multi-conditional time series with higher accuracy. However, they didn’t consider the attention mechanisms to learn temporal features. Besides, the conditioning method for CNN and RNN is not specific, and the number of parameters in each layer is tremendous. This paper proposes the conditioning method for two types of neural networks, and respectively uses the gated recurrent unit network (GRU) and the dilated depthwise separable temporal convolutional networks (DDSTCNs) instead of LSTM and DC-CNN for reducing the parameters. Furthermore, this paper presents the lightweight RNN-based hidden state attention module (HSAM) combined with the proposed CNN-based convolutional block attention module (CBAM) for time series forecasting. Experimental results show our model is superior to other models from the viewpoint of forecasting accuracy and computation efficiency.

scholarly journals Stacked Heterogeneous Neural Networks for Time Series Forecasting

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Florin Leon ◽  
Mihai Horia Zaharia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Case Studies ◽  
Multilayer Perceptron ◽  
Neural Model ◽  
Activation Function ◽  
Time Series Forecasting ◽  
The Other ◽  
Activation Functions

A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

Designing a Neural Network Model for Time Series Forecasting

2020 ◽  
pp. 259-284 ◽  
Author(s):  
Paola Andrea Sanchéz Sanchéz ◽  
José Rafael García González ◽  
Carlos Hernán Fajardo-Toro ◽  
Paloma María Teresa Martínez Sánchez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Forecasting ◽  
Extant Literature ◽  
Statistical Procedures ◽  
Linear Behavior

Artificial neural networks are highly flexible and efficient tools in the approximation of time series patterns. In recent years, more than 5,000 studies oriented to the use of neural networks in time series forecasting have been evidenced in the extant literature. However, the methodology used for its specification and construction still involves a lot of trial and error or is inherited from econometric and statistical procedures that do not fit perfectly to the characteristics of the time series. This is especially true when they present non-linear behavior; moreover, it is not designed for working with neural networks. The objective of this chapter is to present a five-step guide for the specification, design, and validation of a neural network model for forecasting time series.

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.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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.

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