Seasonal drought forecasting in arid regions, using different time series models and RDI index

2019 ◽  
Vol 11 (3) ◽  
pp. 633-654 ◽  
Author(s):  
Mohammad Mehdi Moghimi ◽  
Abdol Rassoul Zarei ◽  
Mohammad Reza Mahmoudi
Keyword(s):  
Time Series ◽  
Ar Model ◽  
Time Series Models ◽  
Climatic Data ◽  
Drought Forecasting ◽  
Seasonal Drought ◽  
Drought Assessment ◽  
Model Based ◽  
Modeling And Forecasting ◽  
Drought Classes

Abstract Confronting drought and reducing its impacts requires modeling and forecasting of this phenomenon. In this research, the ability of different time series models (the ARIMA models with different structures) were evaluated to model and predict seasonal drought based on the RDI drought index in the south of Iran. For this purpose, the climatic data of 16 synoptic stations from 1980 to 2010 were used. Evaluation of time series models was based on trial and error. Results showed drought classes varied between ‘very wet’ to ‘severely dry’. The more occurrence frequency of ‘severely dry’ class compared to other drought classes represent the necessity of drought assessment and the importance of managing the effects of this phenomenon in the study area. Results showed that the highest severity of drought occurred at Abadeh, Shiraz, Fasa, Sirjan, Kerman, Shahre Babak and Saravan stations. According to selecting the best model fitted to the computed three-month RDI time series, results indicated that the MA model based on the Innovations method resulted in maximum cases with the best performance (37.5% of cases). The AR model based on the Yule–Walker method resulted in minimum cases with the best performance (6.3% of cases) in seasonal drought forecasting.

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

scholarly journals Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models

2020 ◽  
Vol 140 ◽  
pp. 110151 ◽  
Author(s):  
Mohsen Maleki ◽  
Mohammad Reza Mahmoudi ◽  
Mohammad Hossein Heydari ◽  
Kim-Hung Pho
Keyword(s):  
Time Series ◽  
Death Rate ◽  
Time Series Models ◽  
Modeling And Forecasting ◽  
The World

Time Series Model Based Earning Forecasting

2013 ◽  
Vol 791-793 ◽  
pp. 2147-2150
Author(s):  
Xiang Rong Jiang ◽  
Ying Ying Cui
Keyword(s):  
Time Series ◽  
Empirical Study ◽  
Relative Stability ◽  
Listed Companies ◽  
Time Series Model ◽  
New Method ◽  
Time Series Models ◽  
Seasonal Patterns ◽  
Model Based ◽  
Over Time

We propose a procedure to forecast earning of listed companies. It is a modification of method developed for forecasting series with stable seasonal patterns. The new method is motivated by the observations that seasonal patterns, which may be evolving over time and remain relative stability, arise in finance market. The method can be applied to forecast individual observations as well as the end-of-season totals. Empirical study will be conducted with data from finance market to evaluate the performance of the proposed method. The new method is proved more effective than traditional time series models.

Differential Autoregressive of Cycle Prediction Model and its Application

2012 ◽  
Vol 198-199 ◽  
pp. 1315-1320 ◽  
Author(s):  
Cai Lian Luo ◽  
Huan Qi ◽  
Su Qin Sun
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Stationary Time Series ◽  
Ar Model ◽  
Short Term ◽  
Basic Principles ◽  
Java Programming ◽  
Modeling And Forecasting ◽  
High Prediction ◽  
Short Term Prediction

AR model is widely used which based on stationary time series used for short-term prediction. However, in fact the time series we got is often non-stationary, and there is little literature researching the smooth processing, modeling and forecasting and then restoring the results in system. In view of this, this paper provides a method, that is, differential autoregressive of cycle prediction. First, explain the basic principles and give the calculation steps of smooth processing, modeling and forecasting and restoring the results. Then, applied the prediction method in the short-term forecast of coal arrive of a provincial. Model implementation is based on java programming. We get high prediction accuracy, the system easily integrated, can be widely used, and can achieve rolling forecast.

Forecasting dirty tanker freight rate index by using stochastic differential equations

2018 ◽  
Vol 05 (04) ◽  
pp. 1850034 ◽  
Author(s):  
Hossein Jafari ◽  
Ghazaleh Rahimi
Keyword(s):  
Time Series ◽  
Differential Equations ◽  
Stochastic Model ◽  
Stochastic Differential Equations ◽  
Simulated Data ◽  
Jump Diffusion ◽  
Freight Rate ◽  
Time Series Models ◽  
Modeling And Forecasting

The accurate forecasting of freight rate index is one of the most important issues in shipping market. The continuous and jump-diffusion stochastic differential equations are used for modeling and forecasting of Baltic exchange Dirty Tanker Index (BDTI). Actual observations and simulated data are applied to estimate the best stochastic model. The comparison of forecasting between SDE methods and the ARIMA time series models show that SDE models have better accuracy than the time series techniques.

scholarly journals NUMERICAL COMPUTATION AND PREDICTION OF ELECTRICITY CONSUMPTION IN TOBACCO INDUSTRY

2017 ◽  
Vol 15 (3) ◽  
pp. 457
Author(s):  
Mirjana Laković ◽  
Ivan Pavlović ◽  
Miloš Banjac ◽  
Milica Jović ◽  
Marko Mančić
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Autoregressive Model ◽  
Moving Average ◽  
Electricity Consumption ◽  
Ar Model ◽  
Model Complexity ◽  
Industrial Plant ◽  
Time Series Models ◽  
The Future

Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict energy consumption for different types of systems and weather conditions. In every industrial plant, electricity consumption presents one of the greatest operating costs. Monitoring and forecasting of this parameter provide the opportunity to rationalize the use of electricity and thus significantly reduce the costs. The paper proposes the prediction of energy consumption by a new time-series model. This involves time series models using a set of previously collected data to predict the future load. The most commonly used linear time series models are the AR (Autoregressive Model), MA (Moving Average) and ARMA (Autoregressive Moving Average Model). The AR model is used in this paper. Using the AR (Autoregressive Model) model, the Monte Carlo simulation method is utilized for predicting and analyzing the energy consumption change in the considered tobacco industrial plant. One of the main parts of the AR model is a seasonal pattern that takes into account the climatic conditions for a given geographical area. This part of the model was delineated by the Fourier transform and was used with the aim of avoiding the model complexity. As an example, the numerical results were performed for tobacco production in one industrial plant. A probabilistic range of input values is used to determine the future probabilistic level of energy consumption.

scholarly journals Revisión De Los Principales Métodos De Modelamiento Y Predicción De Tráfico Orientados A Plataformas De Transmisión De Video E IPTV Usando Series De Tiempo

2013 ◽  
Vol 2 (16) ◽  
pp. 10
Author(s):  
Bayron Villanueva ◽  
Danilo López-Sarmiento ◽  
Edwin Rivas-Trujillo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
State Of The Art ◽  
Traffic Modeling ◽  
Data Networks ◽  
Time Series Models ◽  
Video Traffic ◽  
Short Introduction ◽  
Sarima Model ◽  
Modeling And Forecasting

En este artículo se hace una investigación de las principales técnicas que existen para modelar y predecir el tráfico de video de forma estadística, enfocándose en los modelos que usan series de tiempo con el fin de identificar cuáles de estos se acomodan mejor al tráfico estocástico representativo de los sistemas IPTV. Para tal fin, se hace una introducción al análisis a través de series de tiempo, y una presentación del estado del arte acerca de modelamiento de tráfico de video sobre redes de datos. De la investigación se concluye que, de los modelos que permiten describir y predecir el tráfico futuro sobre redes de datos, los que se ajustan en una mayor medida a sistemas IPTV son modelos basados en series ARIMA, de estos, el modelo SARIMA podría describir de forma más precisa las tendencias periódicas del tráfico IPTV.AbstractThis paper, intends to review the most important techniques that allow performing statistic video traffic modeling and forecasting, focusing in time series models, so we can identify which models are better to describe the representative IPTV stochastic traffic. For this purpose, we make a short introduction to time series analysis, and a review of the state of the art on video traffic modeling over data networks. From this research we conclude that, of all the available models to describe and forecast network traffic, the more appropriate to use within IPTV systems are ARIMA time series models, from which SARIMA model are the best option.ResumoEste artigo tem como objetivo revisar as principais técnicas existentes para a modelagem e previsão de tráfego estatisticamente vídeo, com foco em modelos usando séries temporais, a fim de identificar quais destes são o tráfego estocástico mais adequado representante sistemas IPTV. Para este fim, uma breve introdução à análise por meio de séries temporais, e uma revisão do estado da arte em modelagem de tráfego de vídeo através de redes de dados. A investigação concluiu que, dos modelos para descrever e prever o futuro de tráfego em redes de dados, que são ajustados a uma maior extensão de sistemas de IPTV são baseados em modelos da série ARIMA, estes modelo SARIMA poderia descrever em mais preciso do tráfego periódico tendências IPTV.

Multifractal Models in Finance

2018 ◽  
Author(s):  
Thomas Lux ◽  
Mawuli Segnon
Keyword(s):  
Time Series ◽  
Turbulent Flows ◽  
Statistical Physics ◽  
Market Volatility ◽  
Asset Market ◽  
Time Series Models ◽  
Policy Makers ◽  
Current State ◽  
Modeling And Forecasting ◽  
Forecasting Volatility

This chapter provides an overview over the recently developed so-called multifractal (MF) approach for modeling and forecasting volatility. For analysts and policy makers, volatility is a key variable for understanding market fluctuations. Analysts need accurate forecasts of volatility for tasks such as risk management, as well as option and futures pricing. In addition, asset market volatility plays an important role in monetary policy. The chapter, then, outlines the genesis of the multifractal approach from similar models of turbulent flows in statistical physics and provides details about different specifications of multifractal time series models in finance, available methods for their estimation, and the current state of their empirical applications.

scholarly journals Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
State Equation ◽  
Ar Model ◽  
Prediction Algorithm ◽  
Time Series Models ◽  
Multiple Model ◽  
Concentration Data ◽  
Interactive Multiple Model

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5is the main particulate matter in air pollution. Therefore, how to predict PM2.5accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5prediction, and it is effective.

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