scholarly journals Exploring geometrical patterns in streamflow time series: utility for forecasting?

2018 ◽  
Vol 49 (6) ◽  
pp. 1724-1739 ◽  
Author(s):  
Ramesh S. V. Teegavarapu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
The United States ◽  
Data Driven ◽  
Storm Events ◽  
Streamflow Prediction ◽  
One Step ◽  
The Neural Networks ◽  
Ann Models ◽  
Improved Performance

Abstract Streamflow time series often provide valuable insights into the underlying physical processes that govern responses of any watershed to storm events. Patterns derived from time series based on repeated structures within these series can be beneficial for developing new or improved data-driven forecasting models. Data-driven models, artificial neural networks (ANN), are developed in the current study for streamflow prediction using input structures that are classified by geometrically similar patterns. A new modular and integrated ANN architecture that combines multiple ANN models, referred to as pattern-classified neural network (PCNN), is proposed, developed and investigated in this study. The PCNN relies on the development of several independent local models instead of one global data-driven prediction model. The PCNN models are evaluated for one step-ahead prediction of daily streamflows for Reed Creek and Little River, Virginia, and Elkhorn Creek, Kentucky in the United States. Results obtained from this study suggest that the use of these patterns has improved the performance of the neural networks in prediction. The improved performance of the PCNN models can be attributed to prior classification of data benefiting generalization abilities. PCNN model outputs can also provide an ensemble of forecasts that help quantify forecast uncertainty.

Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks

2021 ◽  
Author(s):  
Yi-Fan Li ◽  
Bo Dong ◽  
Latifur Khan ◽  
Bhavani Thuraisingham ◽  
Patrick T. Brandt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Social Studies ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Graph Neural Networks

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

BOOSTING PERFORMANCE IN NEURAL NETWORKS

1993 ◽  
Vol 07 (04) ◽  
pp. 705-719 ◽  
Author(s):  
HARRIS DRUCKER ◽  
ROBERT SCHAPIRE ◽  
PATRICE SIMARD
Keyword(s):  
Neural Networks ◽  
Error Rate ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
The United States ◽  
Pac Learning ◽  
Postal Service ◽  
United States Postal Service ◽  
Lower Case ◽  
Improved Performance

A boosting algorithm, based on the probably approximately correct (PAC) learning model is used to construct an ensemble of neural networks that significantly improves performance (compared to a single network) in optical character recognition (OCR) problems. The effect of boosting is reported on four handwritten image databases consisting of 12000 digits from segmented ZIP Codes from the United States Postal Service and the following from the National Institute of Standards and Technology: 220000 digits, 45000 upper case letters, and 45000 lower case letters. We use two performance measures: the raw error rate (no rejects) and the reject rate required to achieve a 1% error rate on the patterns not rejected. Boosting improved performance significantly, and, in some cases, dramatically.

scholarly journals Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Lida Barba ◽  
Nibaldo Rodríguez ◽  
Cecilia Montt
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Traffic Accidents ◽  
Learning Algorithm ◽  
Moving Average ◽  
Arima Model ◽  
Hankel Matrix ◽  
One Step ◽  
Value Decomposition ◽  
Two Stages

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0 : 26%, followed by MA-ARIMA with a MAPE of 1 : 12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15 : 51%.

scholarly journals Simulation and Analysis of Indoor Air Quality in Florida Using Time Series Regression (TSR) and Artificial Neural Networks (ANN) Models

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 952
Author(s):  
He Zhang ◽  
Ravi Srinivasan ◽  
Xu Yang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Time Series Regression ◽  
Ann Models ◽  
Mlr Model

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones.

scholarly journals Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2430
Author(s):  
Saurabh Suradhaniwar ◽  
Soumyashree Kar ◽  
Surya S. Durbha ◽  
Adinarayana Jagarlapudi
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Streaming Data ◽  
Support Vector ◽  
Parametric Approach ◽  
Forecast Horizon ◽  
Non Linear ◽  
One Step

High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.

scholarly journals Going Deep: Graph Convolutional Ladder-Shape Networks

2020 ◽  
Vol 34 (03) ◽  
pp. 2838-2845 ◽  
Author(s):  
Ruiqi Hu ◽  
Shirui Pan ◽  
Guodong Long ◽  
Qinghua Lu ◽  
Liming Zhu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Data Sets ◽  
Great Success ◽  
Graph Classification ◽  
Convolutional Networks ◽  
The Neural Networks ◽  
Node Classification ◽  
Improved Performance ◽  
Laplacian Smoothing

Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric Laplacian smoothing operation to aggregate the feature information of one node with that of its neighbors. While they have achieved great success in semi-supervised node classification on graphs, current approaches suffer from the over-smoothing problem when the depth of the neural networks increases, which always leads to a noticeable degradation of performance. To solve this problem, we present graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance. We have validated the effectiveness of proposed GCLN at a node-wise level with a semi-supervised task (node classification) and an unsupervised task (node clustering), and at a graph-wise level with graph classification by applying a differentiable pooling operation. The proposed GCLN outperforms original GCNs, deep GCNs and other state-of-the-art GCN-based models for all three tasks, which were designed from various perspectives on six real-world benchmark data sets.

scholarly journals Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance

2019 ◽  
Vol 61 ◽  
pp. 01031 ◽  
Author(s):  
Jaromír Vrbka ◽  
Zuzana Rowland ◽  
Petr Šuleř
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
International Relations ◽  
Trade Balance ◽  
Point Of View ◽  
Republic Of China ◽  
China Trade ◽  
The Neural Networks ◽  
Time Series Alignment ◽  
The Eu

China, by GDP, is the second largest economic power, and hence also a key player in the field of international relations. As far as the EU is concerned, it is China's largest trading partner. From this point of view, it is clear that monitoring export and import development between these partners is essential. This paper therefore aims to compare two useful methods, namely the accuracy of time series alignment through regression analysis and artificial neural networks, to assess the evolution of the EU and the People's Republic of China trade balance. Data on the export and import trends of these two partners since 2000 have been used, and it is clear that the trade balance was completely different that year than it is now. The development over time is interesting. The most appropriate curve is selected from the linear regression, and from the neural networks three useful neural structures are selected. We also look at the prediction of future developments while taking into account seasonal fluctuations.

scholarly journals DeepLSF: Fusing Knowledge and Data for Time Series Forecasting

2021 ◽  
Author(s):  
Muhammad Ali Chattha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Real World ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Data Driven

This work presents DeepLSF, a framework for time series forecasting that fuses knowledge driven techniques with data driven neural networks. The proposed framework achieves State-Of-The-Art results on three different real world time series forecasting datasets.

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