Moving average models—time series in m-dimensions

1980 ◽  
Vol 9 (5) ◽  
pp. 467-489 ◽  
Author(s):  
D.A. Voss ◽  
C.A. Oprian ◽  
L.A Aroian
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Moving Average Models

scholarly journals Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

Buildings ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 152
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu ◽  
Huirong Zhang
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Transfer Function ◽  
Moving Average ◽  
Cost Data ◽  
Autoregressive Integrated Moving Average ◽  
Work Volume ◽  
Moving Average Models ◽  
Function Models ◽  
Transfer Function Models

An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project.

scholarly journals Excess cases of influenza and the coronavirus epidemic in Catalonia: a time-series analysis of primary-care electronic medical records covering over 6 million people

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e039369 ◽  
Author(s):  
Ermengol Coma Redon ◽  
Nuria Mora ◽  
Albert Prats-Uribe ◽  
Francesc Fina Avilés ◽  
Daniel Prieto-Alhambra ◽  
...  
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Time Series ◽  
Moving Average ◽  
Epidemic Curve ◽  
Imported Case ◽  
Moving Average Models ◽  
Community Transmission ◽  
Auto Regressive ◽  
Primary Care Practices

ObjectivesThere is uncertainty about when the first cases of COVID-19 appeared in Spain. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and estimate numbers of undetected COVID-19 cases.DesignTime-series study of influenza and COVID-19 cases, 2010–2020.SettingPrimary care, Catalonia, Spain.ParticipantsPeople registered in primary-care practices, covering >6 million people and >85% of the population.Main outcome measuresWeekly new cases of influenza and COVID-19 clinically diagnosed in primary care.AnalysesDaily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects. Epidemic curves were characterised for the 2010–2011 to 2019–2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019–2020 season were used to model expected case numbers with Auto Regressive Integrated Moving Average models, overall and stratified by age. Daily excess influenza cases were defined as the number of observed minus expected cases.ResultsFour influenza season curves (2011–2012, 2012–2013, 2013–2014 and 2016–2017) were used to estimate the number of expected cases of influenza in 2019–2020. Between 4 February 2020 and 20 March 2020, 8017 (95% CI: 1841 to 14 718) excess influenza cases were identified. This excess was highest in the 15–64 age group.ConclusionsCOVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. The use of clinical codes could misrepresent the true occurrence of the disease. Serological or PCR testing should be used to confirm these findings. In future, this surveillance of excess influenza could help detect new outbreaks of COVID-19 or other influenza-like pathogens, to initiate early public health responses.

Statistical modelling of spatial variability of undrained strength

1992 ◽  
Vol 29 (5) ◽  
pp. 721-729 ◽  
Author(s):  
V. Ravi
Keyword(s):  
Time Series ◽  
Spatial Variability ◽  
Computer Program ◽  
Moving Average ◽  
Nonlinear Least Squares ◽  
Autoregressive Moving Average ◽  
Spatial Series ◽  
Moving Average Models ◽  
Undrained Strength ◽  
Autoregressive Moving Average Models

Spatial variability of undrained strength (Cu) has been modelled in several ways in the past. In particular, concepts of time series such as autoregressive moving average models have been used to model the analogous "spatial series" of the values of depth versus undrained strength. It should be noted that the very purpose of such modelling studies is to provide estimates of the values of undrained strength at a given value of depth. In the present paper, the main prerequisite to apply these models, viz. the complete removal of trend present in the spatial series of depth versus Cu, has been focussed. An accurate modelling procedure is recommended which can estimate the values of Cu at a given value of depth better than any other model in this class of models existing in the literature. Sensitivity in the trend patterns of the depth versus Cu data is well taken care of. A computer program has been developed in FORTRAN 77to fit the model in conjunction with a standard nonlinear least-squares routine taken from the literature. One of the advantages of the present model is the speed of convergence of the computer program. Two case studies appearing in the literature have been successfully solved to demonstrate the efficacy of the model developed. Key words : spatial variability, time series analysis, spatial series, nonstationarity, autoregressive moving average models, regression, nonlinear least squares, error sum of squares.

Space-time evaluation of reservoir regulation policies

1984 ◽  
Vol 16 (1) ◽  
pp. 21-21 ◽  
Author(s):  
Stuart J. Deutsch ◽  
José A. Ramos
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Moving Average ◽  
Space Time ◽  
Autoregressive Integrated Moving Average ◽  
Hydrologic Time Series ◽  
Temporal Correlations ◽  
Reservoir Regulation ◽  
Moving Average Models ◽  
Time Evaluation

Stochastic modeling of vector hydrologic time series exhibiting spatial as well as temporal correlations is examined with the general class of STARIMA, space-time autoregressive integrated moving-average models.

scholarly journals Investigation of Global Annual Temperature Anomalies and Statistical Inferences Based On It

2011 ◽  
Vol 50 (1) ◽  
pp. 23-31
Author(s):  
Julija Važnevičiūtė ◽  
Nerutė Kligienė
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Moving Average ◽  
Arma Models ◽  
One Dimensional ◽  
Statistical Inferences ◽  
Moving Average Models ◽  
Average Variation ◽  
Predicted Values ◽  
North And South

Global warming problem earlier investigated mostly by scientists in climatology, now attract the attention of manyresearchers because a changing climate cause a great anxiety. The paper analyses the data of North and South hemisphere temperaturesvariation and North and South hemisphere temperatures 5 years average variation. The data collected by the National Aeronauticsand Space Administration since 1880 year are analyzed as time series in this paper. The ARMA models are fitted, their statisticalcharacteristics evaluated and predictions for future values have been calculated using the fitted models of one dimensional and multidimensionalautoregressive models. Calculated predicted values were compared to really observed values and the research resulted inthe best fitted autoregressive and moving average models well describing the global warming data.

scholarly journals Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11537
Author(s):  
Navid Feroze ◽  
Kamran Abbas ◽  
Farzana Noor ◽  
Amjad Ali
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Time Series Models ◽  
Absolute Increase ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Structural Time Series ◽  
Moving Average Models ◽  
Structural Time Series Models ◽  
Structural Time

Background COVID-19 is currently on full flow in Pakistan. Given the health facilities in the country, there are serious threats in the upcoming months which could be very testing for all the stakeholders. Therefore, there is a need to analyze and forecast the trends of COVID-19 in Pakistan. Methods We have analyzed and forecasted the patterns of this pandemic in the country, for next 30 days, using Bayesian structural time series models. The causal impacts of lifting lockdown have also been investigated using intervention analysis under Bayesian structural time series models. The forecasting accuracy of the proposed models has been compared with frequently used autoregressive integrated moving average models. The validity of the proposed model has been investigated using similar datasets from neighboring countries including Iran and India. Results We observed the improved forecasting accuracy of Bayesian structural time series models as compared to frequently used autoregressive integrated moving average models. As far as the forecasts are concerned, on August 10, 2020, the country is expected to have 333,308 positive cases with 95% prediction interval [275,034–391,077]. Similarly, the number of deaths in the country is expected to reach 7,187 [5,978–8,390] and recoveries may grow to 279,602 [208,420–295,740]. The lifting of lockdown has caused an absolute increase of 98,768 confirmed cases with 95% interval [85,544–111,018], during the post-lockdown period. The positive aspect of the forecasts is that the number of active cases is expected to decrease to 63,706 [18,614–95,337], on August 10, 2020. This is the time for the concerned authorities to further restrict the active cases so that the recession of the outbreak continues in the next month.

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