Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey

In the early 2000s, the Republic of Turkey has initiated an ambitious reform program in her electricity market, which requires privatization, liberalization as well as a radical restructuring. The most controversial reason behind, or justification for, recent reforms has been the rapid electricity demand growth; that is to say, the whole reform process has been a part of the endeavors to avoid the so-called “energy crisis”. Using cointegration analysis and autoregressive integrated moving average (ARIMA) modelling, the present article focuses on this issue by both providing an electricity demand estimation and forecast, and comparing the results with official projections. The study concludes, first, that consumers’ respond to price and income changes is quite limited and therefore there is a need for economic regulation in Turkish electricity market; and second, that the current official electricity demand projections highly overestimate the electricity demand, which may endanger the development of both a coherent energy policy in general and a healthy electricity market in particular.     

我国煤炭消费量:ARIMA模型预测效果分析

采用ARIMA模型对1953年以来我国煤炭消费量的年度数据进行分析。与多种模型相比较,ARIMA模型不但适合于我国煤炭消费量的非平稳时间序列的特点,并且预测效果比较理想。结果表明,ARIMA（3,1,3）模型预测2002～2005年数值平均,预测误差仅为3.981%,可用于未来我国煤炭消费量的预测。     

基于小波分析的LS-SVM—ARIMA组合模型的黄金价格预测

黄金作为一种特殊的金融商品,其价格受国际原油、美元汇率、通货膨胀等多种因素的影响,波动性强。使用单一模型进行黄金价格预测通常效果不佳,只有充分考虑价格变化的各个方面才能更加准确地预测黄金价格。应用小波分析将黄金价格分解为4个不同变化趋势,应用LS-SVM与ARIMA模型对不同变化趋势进行建模预测,并重构黄金价格组合预测的结果。实证结果表明,该组合模型预测精度比单一模型预测精度高。     

A PROTOTYPICAL SEASONAL ADJUSTMENT MODEL

Abstract. The paper analyses unobserved-components modelling and estimation for the simplest ARIMA process that accepts a full decomposition into trend, seasonal and irregular components. This prototypical model exemplifies many features of and issues arising in model-based seasonal adjustment that are less transparent in more complex seasonal time series models. In particular the analysis illuminates the major issues surrounding the specification of the component models and the identification of a unique structure for them. In so doing, the relationship between reduced- and structural-form approaches to unobserved components estimation is illustrated within an ARIMA-modelling framework. Finally, the properties of the minimum mean-squared-error estimators of the unobserved components are examined and the two main types of estimation error, revisions in the preliminary estimator and error in the final estimator, are analysed.     

DETERMINING THE DEGREE OF DIFFERENCING FOR TIME SERIES VIA THE LOG SPECTRUM

Abstract. While many time series require differencing before a model may be fitted it has been shown that 'overdifferencing' may result in a fitted model with poor long term forecasting properties. This may present real problems when the degree of differencing which is appropriate is fractional. We show that the log spectrum is a natural quantity to consider when attempting to determine the degree of differencing required and outline the distribution theory required. The ideas are shown to extend to the seasonal case and can be used to assess whether seasonal differencing is appropriate.     

Time series study of the impact of serious fires on fire occurrence statistics in cities of Jiangsu

In this paper, the time series method is used to analyze the effect of fire disasters and serious fires on monthly fire occurrence statistics in 10 cities from January 1997 to December 2001 in Jiangsu province. After removal of outliers in the irregular component of the original data series and seasonal adjustments, the intervention model is applied to quantify the effect of the fire disasters and serious fires on the total monthly fire statistics in the cities of Jiangsu province. The results show that the impact on monthly fires lasts for 3 months for eight cities and 2 months for two cities. Other phenomena are also detected in these 10 cities. Finally, some explanations for these phenomena are proposed.     

移动网无线信号变化预测研究

鉴于移动通信网规模日益扩大,网络运营状态不易即时掌控的现状,提出了一种用于观测移动网无线信号实时变化的新型监控系统。在该系统长期运行而获得的采样数据基础上,对某处小区的无线信号变化特性进行了研究。运用SPSS统计工具和时间序列中的Box—Jenkins的建模方法,分别建立了AR、MA、ARMA、ARIMA模型对实测数据进行了分析和预测,然后对不同模型的预测结果进行了误差分析,结果表明ARIMA（1,1,1）模型准确性最高,误差最小,能对短期内的无线信号变化趋势进行预测。     

基于ARIMA模型的渔业经济预测及其优化

渔业作为国民经济的重要基础之一，对其进行预测十分必要。本文采用时间序列ARIMA模型对渔业总产值进行预测，根据模型预测结果进行误差分析。考虑通货膨胀对预测模型的影响，利用居民消费价格指数（CPI）对模型进行进一步优化。进而以江苏省渔业总产值为例，将1995-2014年的数据作为训练样本，建立模型并结合CPI指数对其优化，以2015-2018年数据作为测试样本，验证了优化模型具有较好的预测效果。     

基于ARIMA时间序列模型的滑坡位移预测预报——以三峡库区王家坡滑坡为例

为深入研究滑坡的位移特性,以三峡库区王家坡滑坡为例,基于简单移动平均法及时间序列的减法模型分解滑坡位移的趋势项位移和周期项位移,对趋势项位移采取多项式拟合处理,对周期项位移进行一阶差分处理,并分析其自相关函数ACF及偏自相关函数PACF,然后根据模型的识别规则,建立了ARIMA(1,1,1)模型。根据时间序列的加法模型,将两部分预测位移相加即为滑坡位移预测的总位移。模型与实测结果对比表明,该预测预报模型效果较好,基本反映了滑坡位移的整体趋势,且实现了对滑坡累积位移的滚动预测,在滑坡累积位移短期预测预报中具有一定的适用性。     

Prediction of BRIC Stock Price Using ARIMA,SutteARIMA, and Holt-Winters

The novel coronavirus has played a disastrous role in many countries worldwide. The outbreak became a major epidemic, engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline. This paper identifies two different models to capture the trend of closing stock prices in Brazil (BVSP), Russia (IMOEX.ME), India (BSESN), and China (SSE), i.e., (BRIC) countries. We predict the stock prices for three daily time periods, so appropriate preparations can be undertaken to solve these issues. First, we compared the ARIMA, SutteARIMA and Holt-Winters (H-W) methods to determine the most effective model for predicting data. The stock closing price of BRIC country data was obtained from Yahoo Finance. That data dates from 01 November 2019 to 11 December 2020, then divided into two categories--training data and test data. Training data covers 01 November 2019 to 02 December 2020. Seven days (03 December 2020 to 11 December 2020) of data was tested to determine the accuracy of the models using training data as a reference. To measure the accuracy of the models, we obtained the means absolute percentage error (MAPE) and mean square error (MSE). Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price (BVSP) while MAPE (0.50) and MSE (579272.65) with Holt-Winters (smaller than ARIMA and SutteARIMA), model SutteARIMA was found most appropriate to predict the stock prices of Russia (IMOEX.ME), India (BSESN), and China (SSE) when compared to ARIMA and Holt-Winters. MAPE and MSE with SutteARIMA: Russia (MAPE:0.7; MSE:940.20), India (MAPE:0.90; MSE:207271.16), and China (MAPE: 0.72; MSE: 786.28). Finally, Holt-Winters predicted the daily forecast values for the Brazil stock price (BVSP) (12 December to 14 December 2020 i.e., 115757.6, 116150.9 and 116544.1), while SutteARIMA predicted the daily forecast values of Russia stock prices (IMOEX.ME) (12 December to 14 December 2020 i.e., 3238.06, 3241.54 and 3245.01), India stock price (BSESN) (12 December to 14 December 2020 i.e.,. 45709.38, 45828.71 and 45948.05), and China stock price (SSE) (11 December to 13 December 2020 i.e., 3397.56, 3390.59 and 3383.61) for the three time periods.