An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

Network Traffic Time Series Performance Analysis Using Statistical Methods

This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting. 

NDVI Short-Term Forecasting Using Recurrent Neural Networks

In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series

Modelling and characterization of fine Particulate Matter dynamics in Bujumbura using low cost sensors

Air pollution is a result of multiple sources including both natural and anthropogenic activities. The rapid urbanization of the cities such as Bujumbura economic capital of Burundi, is one of these factors. The very first characterization of the spatio-temporal variability of PM2.5 in Bujumbura and the forecasting of PM2.5 concentration have been conducted in this paper using data collected during a year, from august 2022 to august 2023, by low cost sensors installed in Bujumbura city. For each commune, an hourly, daily and seasonal analysis were carried out and the results showed that the mass concentrations of PM2.5 in the three municipalities differ from one commune to another. The average hourly and annual PM2.5 concentrations exceed the World Health Organization standards. The range is between 28.3 and 35.0 microgram/m3 . In order to make prediction of PM2.5 concentration, an investigation of RNN with Long Short Term Memory (LSTM) has been undertaken

An intelligent shell game optimization based energy consumption analytics model for smart metering data

Smart metering is a hot research topic and has gained significant attention since the electromechanical metering is not reliable and requires more energy and time. All the existing methods are focused only on how to deal with data rather than how to do efficiently. Prediction of electricity consumption is essential to gain intelligence to the smart gird. Precise electricity prediction allows a service provided in resource planning and also controlling actions for the demand and supply balancing. The users are beneficial from the smart metering solution by effective interpretation of their energy utilization, and labelling them to efficiently handle the utilization cost. With this motivation, the paper presents intelligent energy consumption analytics using smart metering data (ECA-SMD) model to determine the utilization of energy. The presented ECA-SMD model involves three major processes namely data pre-processing, feature extraction, classification, and parameter optimization. The presented ECA-SMD model uses Extreme Learning Machine (ELM) based classification to determine the optimum class labels. Besides, shell game optimization (SGO) algorithm is applied for tuning the parameters involved in the ELM and boosts the classification efficiency. The efficacy of the ECA-SMD model is validated using an extensive set of smart metering data and the results are investigated based on accuracy and mean square error (MSE). The proposed model exhibited supremacy with the maximum accuracy of 65.917 % and minimum MSE of 0.096

A New Hybrid Methodology for Nonlinear Time Series Forecasting

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. This is the reason that hybrid methodologies combining linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. Despite of all advantages of the traditional methodologies for combining ARIMA and ANNs, they have some assumptions that will degenerate their performance if the opposite situation occurs. In this paper, a new methodology is proposed in order to combine the ANNs with ARIMA in order to overcome the limitations of traditional hybrid methodologies and yield more general and more accurate hybrid models. Empirical results with Canadian Lynx data set indicate that the proposed methodology can be a more effective way in order to combine linear and nonlinear models together than traditional hybrid methodologies. Therefore, it can be applied as an appropriate alternative methodology for hybridization in time series forecasting field, especially when higher forecasting accuracy is needed

A comparative assessment of frequentist forecasting models: Evidence from the S&P 500 pharmaceuticals index

This paper compares three forecasting methods, the autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), and neural network autoregression (NNAR) methods, using the S&P 500 Pharmaceuticals Index. The objective is to identify the most accurate model based on the mean average forecasting error (MAFE). The results consistently show the NNAR model to outperform ARIMA and GARCH and to exhibit a significantly lower MAFE. The existing literature presents conflicting findings on forecasting model accuracy for stock indexes. While studies have explored various models, no universally applicable model exists. Therefore, a comparative analysis is crucial. The methodology includes data collection and cleaning, exploratory analysis, and model building. The daily closing prices of pharmaceutical stocks from the S&P 500 serve as the dataset. The exploratory analysis reveals an upward trend and increasing heteroscedasticity in the pharmaceuticals index, with the unit root tests confirming non-stationarity. To address this, the dataset has been transformed into stationary returns using logarithmic and differencing techniques. Model building involves splitting the dataset into training and test sets. The training set determines the best-fit models for each method. The models are then compared using MAFE on the test set, with the model possessing the lowest MAFE being considered the best. The findings provide insights into model accuracy for pharmaceutical industry indexes, aiding investor predictions, with the comparative analysis emphasizing tailored forecasting models for specific indexes and datasets

Peramalan forex syariah menggunakan jaringan saraf tiruan backpropagation

Perkembangan dunia teknologi informasi telah merambah dalam bidang ekonomi, salah satunya adalah pertukaran mata uang asing (forex trading). Beragam tools dibuat untuk memudahkan dalam memulai proses trading, baik dari sisi transaksi hingga peramalan harga masa depan. Analisa fundamental dan teknikal sering digunakan untuk membuat keputusan pertama dalam menentukan posisi beli atau jual. Hal ini bertujuan membantu seorang trader dalam membuat keputusan secara cepat dan tepat. Dalam penelitian ini, metode jaringan saraf tiruan(JST) backpropagation diterapkan untuk meramalkan trading syariah. Hasil percobaan menunjukkan bahwa JST backpropagation dengan arsitektur 4-8-1 merupakan model terbaik untuk peramalan. Hal ini ditunjukkan dengan akurasi peramalan berupa mean squared error (MSE) bernilai 0.00274

Peramalan forex syariah menggunakan jaringan saraf tiruan backpropagation

Perkembangan dunia teknologi informasi telah merambah dalam bidang ekonomi, salah satunya adalah pertukaran mata uang asing (forex trading). Beragam tools dibuat untuk memudahkan dalam memulai proses trading, baik dari sisi transaksi hingga peramalan harga masa depan. Analisa fundamental dan teknikal sering digunakan untuk membuat keputusan pertama dalam menentukan posisi beli atau jual. Hal ini bertujuan membantu seorang trader dalam membuat keputusan secara cepat dan tepat. Dalam penelitian ini, metode jaringan saraf tiruan(JST) backpropagation diterapkan untuk meramalkan trading syariah. Hasil percobaan menunjukkan bahwa JST backpropagation dengan arsitektur 4-8-1 merupakan model terbaik untuk peramalan. Hal ini ditunjukkan dengan akurasi peramalan berupa mean squared error (MSE) bernilai 0.00274