scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

scholarly journals Tourism and Big Data: Forecasting with Hierarchical and Sequential Cluster Analysis

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Big Data ◽  
Time Series Data ◽  
Cluster Method ◽  
Series Data ◽  
Analysis Method ◽  
Simulation And Training ◽  
The Matrix ◽  
Dynamic Forecasting ◽  
And Training ◽  
Hotel Accommodation

A new Big Data cluster method was developed to forecast the hotel accommodation market. The simulation and training of time series data are from January 2008 to December 2019 for the Spanish case. Applying the Hierarchical and Sequential Clustering Analysis method represents an improvement in forecasting modelling of the Big Data literature. The model is presented to obtain better explanatory and forecasting capacity than models used by Google data sources. Furthermore, the model allows knowledge of the tourists’ search on the internet profiles before their hotel reservation. With the information obtained, stakeholders can make decisions efficiently. The Matrix U1 Theil was used to establish a dynamic forecasting comparison.

scholarly journals Prediction of dynamic responses of floating structures using NARX with mirroring technique

2018 ◽  
Vol 203 ◽  
pp. 01025
Author(s):  
Ruly Irawan ◽  
Mohd Shahir Liew ◽  
Montasir Osman Ahmed Ali ◽  
Ahmad Mohamad Al Yacouby
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Dynamic Network ◽  
Dynamic Responses ◽  
Series Data ◽  
Ann Model ◽  
Floating Structure ◽  
Floating Structures ◽  
Time Series Modelling ◽  
Mirroring Technique

Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.

scholarly journals Comparison of autoregressive moving average and state space methods for monthly time series modelling of Labrador and South-East Quebec river flows

2015 ◽  
Vol 51 (3) ◽  
pp. 200-218 ◽  
Author(s):  
Carissa Sparkes ◽  
Leonard M. Lye ◽  
Susan Richter
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Hydrologic Modelling ◽  
Stream Flows ◽  
Time Series Modelling ◽  
High Level ◽  
Accuracy Of Prediction

Time series data such as monthly stream flows can be modelled using time series methods and then used to simulate or forecast flows for short term planning. Two methods of time series modelling were reviewed and compared: the well-known auto regressive moving average (ARMA) method and the state-space time-series (SSTS) method. ARMA has been used in hydrology to model and simulate flows with good results and is widely accepted for this purpose. SSTS modelling is a more recently developed method that is relatively unused for hydrologic modelling. This paper focuses on modelling the stream flows from basins of different sizes using these two time series modelling methods and comparing the results. Three rivers in Labrador and South-East Quebec were modelled: the Romaine, Ugjoktok and Alexis Rivers. Both models were compared for accuracy of prediction, ease of software use and simplicity of model to determine the preferred time series methodology approach for modelling these rivers. The SSTS was considered very easy to use but model diagnostics were found to require a high level of statistical understanding. Ultimately, the ARMA method was determined to be the better method for the typical engineer to use, considering the diagnostics were simple and the monthly flows could be easily simulated to verify results.

scholarly journals Gaussian processes for time-series modelling

2013 ◽  
Vol 371 (1984) ◽  
pp. 20110550 ◽  
Author(s):  
S. Roberts ◽  
M. Osborne ◽  
M. Ebden ◽  
S. Reece ◽  
N. Gibson ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Processes ◽  
Domain Knowledge ◽  
Time Series Data ◽  
Process Models ◽  
Series Data ◽  
Parametric Modelling ◽  
Case Examples ◽  
Time Series Modelling ◽  
Time Series Data Analysis

In this paper, we offer a gentle introduction to Gaussian processes for time-series data analysis. The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes . We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the approaches.

scholarly journals Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

2020 ◽  
Vol 12 (23) ◽  
pp. 4000
Author(s):  
Petteri Nevavuori ◽  
Nathaniel Narra ◽  
Petri Linna ◽  
Tarmo Lipping
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Yield ◽  
Time Series Data ◽  
Weather Data ◽  
Series Data ◽  
Percentage Error ◽  
Time Series Modelling ◽  
Spatio Temporal ◽  
3D Cnn

Unmanned aerial vehicle (UAV) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications. At the same time, the increasing availability of yield monitoring devices in harvesters enables input-target mapping of in-season RGB and crop yield data in a resolution otherwise unattainable by openly availabe satellite sensor systems. Using time series UAV RGB and weather data collected from nine crop fields in Pori, Finland, we evaluated the feasibility of spatio-temporal deep learning architectures in crop yield time series modelling and prediction with RGB time series data. Using Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks as spatial and temporal base architectures, we developed and trained CNN-LSTM, convolutional LSTM and 3D-CNN architectures with full 15 week image frame sequences from the whole growing season of 2018. The best performing architecture, the 3D-CNN, was then evaluated with several shorter frame sequence configurations from the beginning of the season. With 3D-CNN, we were able to achieve 218.9 kg/ha mean absolute error (MAE) and 5.51% mean absolute percentage error (MAPE) performance with full length sequences. The best shorter length sequence performance with the same model was 292.8 kg/ha MAE and 7.17% MAPE with four weekly frames from the beginning of the season.

scholarly journals General Quantile Time Series Regressions for Applications in Population Demographics

2018 ◽  
Author(s):  
Gareth Peters
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Unified Framework ◽  
Population Demographics ◽  
General Audience ◽  
Time Series Modelling ◽  
Morbidity Data ◽  
Core Components

This paper has three objectives, the first is to present a detailed overview in the form of a tutorial for the developments of several key quantile time series modelling approaches. The second objective is to develop a general framework to represent such quantile models in a unifying manner in order to easily develop extensions and connections between existing models that can then be developed to further extend these models in practice. In this regard, the core theme of the paper is to provide perspectives to a general audience of core components that go into construction of a quantile time series model and then to explore each of these core components in detail. The paper is not addressing the concerns of estimation of these models, as there is existing literature on these aspects in many settings, we provide references to relevant works on these aspects in several classes of model. Instead, the focus is rather to provide a unified framework to construct such models for practitioners, therefore the focus is instead on the properties of the models and links between such models from a constructive perspective. The third objective is to compare and discuss the application of the different quantile time series models on several sets of interesting demographic and mortality based time series data sets of relevance to life insurance analysis. The exploration included detailed mortality, fertility, births and morbidity data in several countries with more detailed analysis of regional data in England, Wales and Scotland.

scholarly journals Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Kassim Tawiah ◽  
Wahab Abdul Iddrisu ◽  
Killian Asampana Asosega
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Negative Binomial ◽  
Time Series Data ◽  
Partial Likelihood ◽  
Series Data ◽  
Number Of Zeros ◽  
Time Series Modelling ◽  
Poisson Autoregressive Model ◽  
Excessive Number

Discrete count time series data with an excessive number of zeros have warranted the development of zero-inflated time series models to incorporate the inflation of zeros and the overdispersion that comes with it. In this paper, we investigated the characteristics of the trend of daily count of COVID-19 deaths in Ghana using zero-inflated models. We envisaged that the trend of COVID-19 deaths per day in Ghana portrays a general increase from the onset of the pandemic in the country to about day 160 after which there is a general decrease onward. We fitted a zero-inflated Poisson autoregressive model and zero-inflated negative binomial autoregressive model to the data in the partial-likelihood framework. The zero-inflated negative binomial autoregressive model outperformed the zero-inflated Poisson autoregressive model. On the other hand, the dynamic zero-inflated Poisson autoregressive model performed better than the dynamic negative binomial autoregressive model. The predicted new death based on the zero-inflated negative binomial autoregressive model indicated that Ghana’s COVID-19 death per day will rise sharply few days after 30th November 2020 and drastically fall just as in the observed data.

Estimation of Wave Height Return Periods Using a Nonstationary Time Series Modelling

2006 ◽  
Author(s):  
Christos N. Stefanakos ◽  
Vale´rie Monbet
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Time Series Data ◽  
Nonstationary Time Series ◽  
Series Data ◽  
Dependence Structure ◽  
Return Periods ◽  
Significant Wave ◽  
Time Series Modelling

A new method for calculating return periods of various level values from nonstationary time series data is presented. The key-idea of the method is a new definition of the return period, based on the Mean Number of Upcrossings of the level x* (MENU method). The whole procedure is numerically implemented and applied to long-term measured time series of significant wave height. The method is compared with other more classical approaches that take into acount the time dependance for time series of significant wave height. Estimates of the extremal index are given and for each method bootstrap confidence intervals are computed. The predictions obtained by means of MENU method are lower than the traditional predictions. This is in accordance with the results of other methods that take also into account the dependence structure of the examined time series.

scholarly journals Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: a comparative analysis across two New York hospitals

2021 ◽  
Author(s):  
Ben Lambert ◽  
Isaac J. Stopard ◽  
Amir Momeni-Boroujeni ◽  
Rachelle Mendoza ◽  
Alejandro Zuretti
Keyword(s):  
New York ◽  
Time Series ◽  
Mortality Risk ◽  
Time Series Data ◽  
Medical Center ◽  
New York State ◽  
Prognostic Models ◽  
Series Data ◽  
Clinical Measurements ◽  
The Individual

AbstractA large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient’s hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. In doing so, we quantified the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.4% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (79.2%) versus those who died (61.0%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.

Time-Series Modelling in a Regional Economy: An Exposition of Box—Jenkins Techniques

1981 ◽  
Vol 13 (5) ◽  
pp. 635-644 ◽  
Author(s):  
T Cook ◽  
P Falchi
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Economic Activity ◽  
Regional Economy ◽  
Time Series Data ◽  
Series Data ◽  
Household Formation ◽  
Regional Economic ◽  
Time Series Modelling

Box-Jenkins techniques are shown to be a useful tool for analyzing regional economic activity. This paper identifies temporal, spatial, and causal properties derived from these techniques. A case study is presented illustrating the univariate, transfer-function, multivariate, and multivariate transfer-function methods with household formation and employment time-series data.

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