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This paper explores multifractal analysis for the selected time series water pollution data set and further prediction based on BOD measure with ARFIMA-based fractal model. MFDFA multifractal algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA. The obtained results are compared with similar obtained with autoregressive ARIMA model and basic ARFIMA fractal model. The study reveals an enhancement in accuracy with the use of combination of multifractal analysis and fractal methods for water pollution prediction

Time series forecasting is a crucial task in economics, business, and finance. Traditionally, forecasting methods such as autoregression (AR), moving average (MA), exponential smoothing (SES), and, most commonly, the autoregressive integrated moving average (ARIMA) model are used. The ARIMA model has demonstrated high accuracy in predicting future time series values. With the advancement of computational power and deep learning algorithms, new approaches to forecasting have emerged.

Nowadays, neural networks are among the most popular analysis tools.  They are effective in solving classification, pattern recognition, and clustering problems.  This paper provides a detailed description and analysis of the operational principles of two neural networks, namely a Siamese network and a multilayer perceptron.  A model for using these neural networks in time series forecasting is proposed.  As an example, a web application was created in which the described neural networks were used to analyze the correlation between pairs of financial assets and assess t

It is no doubt challenging to forecast the stock market accurately in reality due to the ever-changing market.  Ever since Artificial Neural Networks (ANNs) have been recognized as universal approximators, they are extensively used in forecasting albeit not having a systematic approach in identifying optimal input.  The appropriate number of significant lags of a time series corresponds to the optimal input in time series forecasting.  Hence, this study aims to compare the effect of several approaches in determining the input lag for ANNs prior to stock market forecasti

The prediction of financial market behaviour constitutes a multifaceted challenge, attributable to the underlying volatility and non-linear characteristics inherent within market data.  Long Short-Term Memory (LSTM) models have demonstrated efficacy in capturing these complexities.  This study proposes a novel approach to enhance LSTM model performance by modulating the learning rate adaptively based on market volatility.  We apply this method to forecast the Kuala Lumpur Composite Index (KLCI), leveraging volatility as a key input to adapt the learning rate during trai

The research results on the effectiveness of using data on solar flares and earthquakes to predict the infrasound wave envelope are presented.  The resulting SARIMAX model, enhanced with the aforementioned external factors, exhibits a 30% reduction in mean squared error and a 29% increase in the coefficient of determination compared to the previously presented ARIMA model.  Additionally, a significant achievement of the new approach, compared to previous ones, is the successful reproduction of the sharp intensity drop in the envelope during the August–September–October