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 modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.     

节气负荷预测方法研究

针对电力负荷预测的实际困难，提出了一种进行负荷预测的新思路，即采用节气负荷作为建模数据，并根据负荷呈现出的较为明显的时序性、周期性特点，将数据分离成趋势分量、节气周期分量，以及时间噪声及白噪声，采用双因子ARIMA模型对数据进行拟合，并以BP网络方法完成负荷预测。据此，着重论述了电力负荷预测中建模数据的选择、预处理方法及其对预测精度的影响。     

网络服务器软件老化现象的测试与分析

提出了一种测试软件老化过程的实验方案，编制了实现该方案的软件工具集，并以应用广泛的Apache网络服务器为目标，研究了当负载为泊松流时服务器的老化现象，用回归分析和ARIMA(自回归差分滑动平均)模型分析了剩余内存的变化情况。结果表明ARIMA模型可以较好地描述和预测内存的使用情况。     

我国发电总量的估测与比较分析

电力工业是国民经济的基础性工业,发电量是电力工业发展水平的一个重要指标,对发电量的准确预测有利于电力工业的可持续健康发展。分别用ARIMA模型和传递函数模型对我国未来5年的发电总量进行估测,并分别对两种模型建模的过程和结果进行比较和分析。两种模型在发电量的预测中各有利弊,根据ACF和PACF,ARIMA模型较好;根据Residual和Fitted,传递函数模型较好。     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.     

基于纵向修正ARIMA的负荷聚合商潜力计算及调峰策略

针对负荷聚合商组织需求侧资源参与调峰市场和电能量市场时,存在的负荷预测准确度不够导致合同购电量误差大、市场界限较为模糊导致考核结算重复等问题,提出了基于纵向修正差分自回归滑动平均模型(Autoregressive Integrated Moving Average model, ARIMA)预测的负荷聚合商潜力计算和调峰策略。首先利用纵向修正ARIMA预测算法预测基线负荷,构建负荷聚合商的调峰潜力测算模型,挖掘需求侧资源的可调节能力为电力市场交易提供数据基础;其次,制定电能量市场与调峰市场的负荷偏差考核方式,在此基础上,以月度滚动时域综合结算收益最大为目标,构建考虑偏差考核的负荷聚合商调峰模型;最后,基于某典型地区负荷聚合商月度历史负荷数据,对所提方法进行算例分析,结果表明所提考虑负荷偏差考核的负荷聚合商调峰策略可以提升聚合商的调峰收益约23.7%,降低负荷聚合商峰谷差约10%,验证了方法的合理性和有效性。 关键词：可调潜力;纵向修正;ARIMA算法;滚动优化;调峰策略;偏差考核 中图分类号：：TM71 文献标志码： A     

CoFEE: Context-aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.     

CBOE Volatility Index Forecasting under COVID-19: An Integrated BiLSTM-ARIMA-GARCH Model

After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The results revealed that during COVID-19, the proposed integrated model, which trained both the Twitter sentiment values and historical VIX values, presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.     

General deterrence effects of U.S. statutory DUI fine and jail penalties: long-term follow-up in 32 states

INTRODUCTION: We examined effects of state statutory changes in DUI fine or jail penalties for firsttime offenders from 1976 to 2002. METHODS: A quasi-experimental time-series design was used (n=324 monthly observations). Four outcome measures of drivers involved in alcohol-related fatal crashes are: single-vehicle nighttime, low BAC (0.01-0.07g/dl), medium BAC (0.08-0.14g/dl), high BAC (>/=0.15g/dl). All analyses of BAC outcomes included multiple imputation procedures for cases with missing data. Comparison series of non-alcohol-related crashes were included to efficiently control for effects of other factors. Statistical models include state-specific Box-Jenkins ARIMA models, and pooled general linear mixed models. RESULTS: Twenty-six states implemented mandatory minimum fine policies and 18 states implemented mandatory minimum jail penalties. Estimated effects varied widely from state to state. Using variance weighted meta-analysis methods to aggregate results across states, mandatory fine policies are associated with an average reduction in fatal crash involvement by drivers with BAC>/=0.08g/dl of 8% (averaging 13 per state per year). Mandatory minimum jail policies are associated with a decline in single-vehicle nighttime fatal crash involvement of 6% (averaging 5 per state per year), and a decline in low-BAC cases of 9% (averaging 3 per state per year). No significant effects were observed for the other outcome measures. CONCLUSIONS: The overall pattern of results suggests a possible effect of mandatory fine policies in some states, but little effect of mandatory jail policies.