Testing for Non-Linear Dependence in Univariate Time Series: An Empirical Investigation of the Austrian Unemployment Rate

The modelling of univariate time series is a subject of great importance in a variety of fields, in regional science and economics, and beyond. Time series modelling involves three major stages:model identification, model%0D estimation and diagnostic checking. This current paper focuses its attention on the model identification stage in general and on the issue of testing for non-linear dependence in particular. If the null hypothesis of independence is rejected, then the alternative hypothesis implies the existence of linear or non-linear dependence. The test of this hypothesis is of crucial importance. If the data are linearly dependent, the linear time series models have to be specified (generally within the SARIMA methodology). If the data are non-linearly dependent, then non-linear time series modelling (such as ARCH, GARCH and autoregressive neural network models) must be employed. Several tests have recently been developed for this purpose. In this paper we make a modest attempt to investigate the power of five competing tests (McLeod-Li-test, Hsieh-test, BDS-test, Terävirta''''s neural network test) in a real world application domain of unemployment rate prediction in order to determine what kind of non-linear specification they have good power against, and which not. The results obtained indicate that that all the tests reject the hypothesis of mere linear dependence in our application. But if interest is focused on predicting the conditional mean of the series, the neural network test is most informative for model identification and its use is therefore highly%0D recommended.

Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques

Synaptic state matching: a dynamical architecture for predictive internal representation and feature perception

Here we consider the possibility that a fundamental function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single parsimonious computational framework. Beyond its utility as a potential model of cortical computation, artificial networks based on this principle have remarkable capacity for internalizing dynamical systems, making them useful in a variety of application domains including time-series prediction and machine intelligence

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling

Kajian Penerapan Metode Peramalan pada Ilmu Ekonomi dan Ilmu Komputer (Studi Kasus : Penerimaan Mahasiswa Baru Ibi Darmajaya)

Forecasting is an activity to predict what might happen in the future. Forecasting is very useful in daily life of personal interests or institutional interests, for example, weather prediction, marketing prediction, earthquakes prediction, prediction of number of students, etc. There are so many forecasting methods in economic science. One which developed by researchers, is quantitative forecasting method. This method is divided into two types. There are regression method and time series method. Time series methodisa methodthat iswidely usedin researchbecause of its capability toresolvea widevarietyof casesand capability to analyzingtime series data. There are several types of time seriesmethods, such as ARIMA, Moving Average, Exponential Smoothing, Time Series Regression, etc. Beside that,forecastingmethodscan alsobe developedin computer scienceby usingthe concept ofartificial intelligence, such as: FuzzyTime Series, NeuralNetworks, andGenetic Algorithm. Econometric modelsusing theconceptof artificial 249intelligenceis able tostudy the behavior ofexisting data, so that the forecasting will be more accurate.Results from this study is the analysis of the application of forecasting methods in the field of economics, especially using econometric regression models and computer science using the concept of artificial intelligence, artificial neural network method

An Interpretable Probabilistic Autoregressive Neural Network Model for Time Series Forecasting

Forecasting time series data presents an emerging field of data science that has its application ranging from stock price and exchange rate prediction to the early prediction of epidemics. Numerous statistical and machine learning methods have been proposed in the last five decades with the demand for generating high-quality and reliable forecasts. However, in real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable, and therefore, hybrid solutions are needed to bridge the gap between classical forecasting methods and scalable neural network models. We introduce an interpretable probabilistic autoregressive neural network model for an explainable, scalable, and "white box-like" framework that can handle a wide variety of irregular time series data (e.g., nonlinearity and nonstationarity). Sufficient conditions for asymptotic stationarity and geometric ergodicity are obtained by considering the asymptotic behavior of the associated Markov chain. During computational experiments, PARNN outperforms standard statistical, machine learning, and deep learning models on a diverse collection of real-world datasets coming from economics, finance, and epidemiology, to mention a few. Furthermore, the proposed PARNN model improves forecast accuracy significantly for 10 out of 12 datasets compared to state-of-the-art models for short to long-term forecasts

COVID-19 time series prediction

The Artificial Neural Network (ANN) is a computer technique that uses a mathematical model to represent a simpler form of the biologic neural structure. It is formed by many processing units and its intelligent behavior comes from the iterations between these units. One application of the ANN is for time series prediction algorithms, where the network learns the behavior of time dependent data and it is able to predict future values. In this work, the ANN is applied in predicting the number of COVID-19 confirmed cases and deaths and also the future seven days for the time series of Brazil, Portugal and the United States. From the simulations it is possible to conclude that the prediction of confirmed cases and deaths from COVID-19 have been successfully made by the ANN. Overall, the ANN with a specific test set had a Mean Squared Error (MSE) 50% higher than the ANN with a random test set. The combination of the sigmoidal and linear activation functions and the Levenberg-Marquardt training function had the lowest MSE for all casesThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data

Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance for a wide-scale application on local loads. To do so, we adopt networks from other short-term load forecasting problems for the multi-step prediction. We evaluate various feed-forward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be ill-chosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using wide-scale smart meters data

KAJIAN PENERAPAN METODE PERAMALAN PADA ILMU EKONOMI DAN ILMU KOMPUTER (Studi Kasus : Penerimaan Mahasiswa Baru IBI Darmajaya)

Forecasting is an activity to predict what might happen in the future. Forecasting is very useful in daily life of personal interests or institutional interests, for example, weather prediction, marketing prediction, earthquakes prediction, prediction of number of students, etc. There are so many forecasting methods in economic science. One which developed by researchers, is quantitative forecasting method. This method is divided into two types. There are regression method and time series method. Time series methodisa methodthat iswidely usedin researchbecause of its capability toresolvea widevarietyof casesand capability to analyzingtime series data. There are several types of time seriesmethods, such as ARIMA, Moving Average, Exponential Smoothing, Time Series Regression, etc. Beside that,forecastingmethodscan alsobe developedin computer scienceby usingthe concept ofartificial intelligence, such as: FuzzyTime Series, NeuralNetworks, andGenetic Algorithm. Econometric modelsusing theconceptof artificial 249intelligenceis able tostudy the behavior ofexisting data, so that the forecasting will be more accurate.Results from this study is the analysis of the application of forecasting methods in the field of economics, especially using econometric regression models and computer science using the concept of artificial intelligence, artificial neural network method.Key words : forecasting, Neural Network, Regressio

ANALYSIS OF STOCK PRICE PREDICTION USING DATA MINING APPROACH

Financial forecasting is one of the most interesting subjects within the area of machine learning studies. Forecasting stock prices is challenging due to the nature of stock prices that are usually non-linear, complex and noisy. This paper would be discussing the most prominent forecasting method which is the time-series forecasting and its machine learning tools used to create the prediction. The aim of this project is to study the data mining approach on predicting stock price that offers accuracy and sustains its reliability in the system. Using Data Mining approach in training the algorithms that will produce the best results based on Public Listed Companies‟ stock price data that dates back until 1998. This system utilizes Artificial Neural Network and Support Vector Machine as its main inference engine with numerous methods to measure the accuracy of both. It is anticipated that this analysis would become a platform for producing a prediction application that is reliable for usage in the future