Forecasting Cryptocurrency Prices Using ARIMA and Neural Network: A Comparative Study

2020 ◽  
Author(s):  
Saurabh Kumar
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Sample Period ◽  
Neural Network Models ◽  
Time Horizons ◽  
The World ◽  
Auto Regressive

The prices of cryptocurrencies are very volatile and forecasting them is a challenging task for the researchers across the world. The present study examines the accuracy of forecasted returns of the two most popular cryptocurrencies (Bitcoin and Ethereum) for the sample period spanning from October 1, 2013, to November 30, 2018. Auto-regressive integrated moving average (ARIMA) and Neural Network models have been used to forecast the returns of the cryptocurrencies. The forecasting results for different time-horizons indicate that for a shorter time-horizon, ARIMA model is better for forecasting the returns of cryptocurrencies, whereas, for a longer time-horizon, Neural Network model is better for forecasting the returns of cryptocurrencies. These results have implications for traders, investors, regulators, policymakers and academia.

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Ning-Kang Pan ◽  
Chunwan Lv
Keyword(s):  
Neural Network ◽  
Policy Making ◽  
Energy Requirement ◽  
Moving Average ◽  
Network Models ◽  
Primary Energy ◽  
Backpropagation Neural Network ◽  
Neural Network Models ◽  
Autoregressive Integrated Moving Average ◽  
Primary Energy Requirement

Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

scholarly journals Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

2022 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Arunkumar P. M. ◽  
Lakshmana Kumar Ramasamy ◽  
Amala Jayanthi M.
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Auto Regressive ◽  
Important Research Area

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

scholarly journals Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 22 ◽  
Author(s):  
Xingkui Xu ◽  
Chunfeng Wu ◽  
Qingyu Hou ◽  
Zhigang Fan
Keyword(s):  
Neural Network ◽  
Error Compensation ◽  
Linear Models ◽  
Arima Model ◽  
Network Models ◽  
Random Component ◽  
Neural Network Models ◽  
Angle Sensor ◽  
Elman Neural Network ◽  
Differential Algorithm

As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.

Malaria Disease Distribution in Sudan Using Time Series ARIMA Model

2015 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Mohammed Ibrahim Musa
Keyword(s):  
African Country ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Climate Factors ◽  
Training Set ◽  
The World ◽  
Auto Regressive ◽  
Sub Saharan ◽  
The Relationship

<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow

2018 ◽  
Vol 33 (1) ◽  
pp. 159-172 ◽  
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Reihaneh Bandari ◽  
Richard C. Peralta
Keyword(s):  
Neural Network ◽  
Lead Time ◽  
Network Models ◽  
Neural Network Models ◽  
Auto Regressive

A review and classification of the existing models of cyanobacteria

2006 ◽  
Vol 30 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Basak Guven ◽  
Alan Howard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Population Dynamics ◽  
Mathematical Models ◽  
Network Models ◽  
Transport Mechanisms ◽  
Neural Network Models ◽  
The World ◽  
Artificial Neural Network Models

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting bloom occurrence in lakes and rivers. In this paper existing key models of cyanobacteria are reviewed, evaluated and classified. Two major groups emerge: deterministic mathematical and artificial neural network models. Mathematical models can be further subcategorized into those models concerned with impounded water bodies and those concerned with rivers. Most existing models focus on a single aspect such as the growth of transport mechanisms, but there are a few models which couple both.

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