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Integration of the Non-linear Time Series GARCH Model with Fuzzy Model Optimized with Water Cycle Algorithm for River Streamflow Forecasting
For managing water resources and operating reservoirs in dynamic contexts, accurate hydrological forecasting is essential. However, it is difficult...
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Mobile Traffic Prediction Based on AR-GARCH-LightGBM Hybrid Model
Accurate prediction of mobile network traffic is the basis for public network planning, mobile base station management and service quality...
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Fuzzy Portfolio with a Novel Power Membership Function Based on GARCH and BlackâLitterman Model
We construct a fuzzy mean-semi-absolute deviation portfolio with novel power membership functions. The portfolio return is measured by the...
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Time Series
Time series algorithms utilize observed past data to make predictions about future values based on their historical patterns and trends. By...
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Determination of Passenger-Carrying Cost of Delhi Redline Metro Using Physical System Theory and Garch Model and the Most Preferred Schedule of Metro Operation
To remove the traffic jam on the roads in Delhi, the metro rail was inducted by the Government. The limitation of previous studies reveals that the...
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Analysing and Predicting Streamwise Velocity Fluctuations in Nonstationary Atmospheric Surface Layers Using the ARMA-GARCH Model
Obtaining reliable statistical results from nonstationary data remains a big challenge in understanding the atmospheric surface layer (ASL). This...
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Towards an efficient machine learning model for financial time series forecasting
Financial time series forecasting is a challenging problem owing to the high degree of randomness and absence of residuals in time series data....
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Co-movements Between Bitcoin and Gold: Multivariate BEKK-GARCH Models
This study examined the Co-movements between gold and Bitcoin using weekly data between January 4, 2015, and May 21, 2023. Multivariate-GARCH models...
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Application of hybridized ANNâGARCH, ANNâSETAR, MARSâSPSO, and CANFISâSPSO meta-models for improving accuracy of monthly streamflow prediction
Among the components of the hydrological cycle, stream flow has a major role in integrated water resources management. Establishing an accurate and...
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Analyzing Stock Market Linkages: Exploring Volatility Spillover Effects Between SGX and NSE Nifty Using ADCC GARCH Model
In the current era of globalized investments, this study investigates the intricate interconnectivity of stock markets among nations, focusing on...
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Signature-Based Community Detection for Time Series
Community detection for time series without prior knowledge poses an open challenge within complex networks theory. Traditional approaches begin by...
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Volatility Clustering in Nifty Energy Index Using GARCH Model
Balaji, Lavanya Anita, H. B. Ashok Kumar, BalajiVolatility has become increasingly important in derivative pricing and hedging, risk management, and...
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Deep Learning Models for Stock Market Forecasting: GARCH, ARIMA, CNN, LSTM, RNN
Stock price prediction has long been a pivotal area of interest for investors, financial analysts, and researchers alike. The ability to forecast...
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Time Series Analysis of Crypto Currency Using ARIMAX
Crypto currency is in trend almost everywhere in the world. The extreme volatile nature of the crypto currencies is the main reason why it has been a...
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Nonlinear autocorrelation function of functional time series
In functional time series analysis, the functional autocorrelation function (fACF) plays an important role in revealing the temporal dependence...
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A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process
The Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time...
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ShortâTerm High-Speed Traffic Flow Prediction Based on ARIMA-GARCH-M Model
The traditional traffic flow prediction model acquired the poor characteristics of the traffic flow time series, which led to the low prediction...
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Motor imagery EEG signal classification with a multivariate time series approach
BackgroundElectroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are...
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Time Series and Statistical Analyses on REIT Stock Prices for Forecasting and Assessing the Impact of COVID-19
In this chapter, we describe the application of Time Series techniques such as ARIMA and ARIMA-GARCH Models to model and forecast the stock prices...
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Analysis and Forecasting of Crude Oil Price Based on Univariate and Multivariate Time Series Approaches
This paper discusses the notion of multivariate and univariate analysis for the prediction of crude oil price in India. The study also looks at the...