Currently, cryptocurrency has become a research and investment topic of great concern attracted considerable attentions in wide range fields. Stocks are well known as form investment, there countless studies on how its price trends. Cryptocurrencies, however, not easily predicted due to their extremely high volatility. This paper implements the forecasting results by taking three major cryptocu...

Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holt’s linear exponential smoothing, and the autoregres...

Stocks are one type of investment that promises return for investors but often carries a high risk. Value at Risk (VaR) is measuring tool can calculate the amount worst loss occurs in stock portfolio with certain level confidence and within time period. In general, financial data have volatility value, which causes residuals not normally distributed. ARCH/GARCH modoel used to solve heteroscedas...

The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ...

Abstract This study focuses on the Indian gold futures market where primary participants hold sentimental value for underlying asset and are globally ranked number two in terms of largest private holdings physical form. The trade relates to seasons, festivity, government policy. So, paper will discuss seasonality intervention analysis. Due non-constant variance, we also use standard variance st...

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...

Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and pre...