The Hybrid ARIMA model

The Cryptocurrency is growing strongly and widely such as Bitcoin (BTC) and Ethereum (ETH) used in the world, which has attracted wide attention of researchers in recent times. In this work, Autoregressive Integrate Moving Average (ARIMA) model, machine learning algorithms, Support Vector Regression (SVR) will be implemented to predict the closing price of The Cryptocurrency the next day.

7p visherylsandberg 18-05-2022 14 2   Download

This study found that the proposed method produced better forecast accuracy than the other three classic methods and the hybrid EMD-ARIMA models.

26p spiritedaway36 28-11-2021 8 1   Download

Indian Robusta Coffee has made a slot for itself in the world market, particularly for its decent blend up quality. In India production of Robusta is more i.e. around 62–65%. Indian coffee prices are often random as they are largely inclined on production, demand of coffee in domestic and world level forces, etc. In this study Hybrid ARIMA-ANN models was compared with ARIMA and ANN model to evaluate the past behaviour of a time series data, in order to make inferences about its future behaviour for Robusta species of Indian coffee.

6p chauchaungayxua8 03-10-2020 10 1   Download

There are several linear time-series forecasting models available in literature. One of the important and widely used technique for analysis of univariate time-series data is Box Jenkins’ Autoregressive integrated moving average (ARIMA) methodology (Box et al., 2007). Sometimes addition of the other exogenous variables increases the prediction accuracy of ARIMA model (ARIMAX). For this aspect we applied different p and q order ARIMAX model for five nutrient combinations of nitrogen content which is further developed by including organic carbons an input (exogenous) variable.

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The fitting curves demonstrate that the Hybrid model produces closer trend so better describing the actual data. Via our study with Vietnam Index, it is confirmed that the characteristics of ARIMA model are more suitable for linear time series while ANN model is good to work with nonlinear time series. The Hybrid model takes into account both of these features, so it could be employed in case of more generalized time series. As the financial market is increasingly complex, the time series corresponding to stock indexes naturally consist of linear and non-linear components.

6p elandorr 03-12-2019 39 0   Download