scholarly journals Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

2021 ◽  
Vol 877 (1) ◽  
pp. 012031
Author(s):  
Salah L. Zubaidi ◽  
Hussein Al-Bugharbee ◽  
Khalid Hashim ◽  
Nabeel Saleem Saad Al-Bdairi ◽  
Sabeeh L. Farhan ◽  
...  
Keyword(s):  
Autoregressive Model ◽  
Model Performance ◽  
Training Sample ◽  
Ar Model ◽  
Maximum Temperature ◽  
Model Order ◽  
Testing Sample ◽  
Wide Range ◽  
Forecasting Performance ◽  
Prediction And Forecasting

Abstract This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model.

scholarly journals Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

2021 ◽  
Vol 877 (1) ◽  
pp. 012033
Author(s):  
Nabeel Saleem Saad Al-Bdairi ◽  
Salah L. Zubaidi ◽  
Hussein Al-Bugharbee ◽  
Khalid Hashim ◽  
Sabeeh L. Farhan ◽  
...  
Keyword(s):  
Air Temperature ◽  
Spectrum Analysis ◽  
Autoregressive Model ◽  
Fitness Function ◽  
Singular Spectrum Analysis ◽  
Maximum Temperature ◽  
Monthly Basis ◽  
Singular Spectrum ◽  
Prediction And Forecasting ◽  
Maximum Air Temperature

Abstract In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings.

scholarly journals Updated Moving Forecasting Model of Air Maximum Temperature

2021 ◽  
Vol 877 (1) ◽  
pp. 012032
Author(s):  
Khalid Hashim ◽  
Hussein Al-Bugharbee ◽  
Salah L. Zubaidi ◽  
Nabeel Saleem Saad Al-Bdairi ◽  
Sabeeh L. Farhan ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
High Performance ◽  
Training Sample ◽  
Temperature Time Series ◽  
Ar Model ◽  
Maximum Temperature ◽  
Forecasting Model ◽  
Temperature Time ◽  
Maximum Air Temperature

Abstract In the current study, a moving forecasting model is used for the purpose of forecasting maximum air temperature. A number of recordings are used for building the AR model and next, to forecasting some temperature values ahead. Then the AR model coefficients are updating due to shifting the training sample by adding new temperature values in order to involve the change in temperature time series behaviour. The current work shows a high performance all over the temperature time series, which considered in the analysis.

scholarly journals A Direct-Steam Linear Fresnel Performance Model for NREL’S System Advisor Model

2012 ◽  
Author(s):  
Michael J. Wagner ◽  
Guangdong Zhu
Keyword(s):  
Heat Loss ◽  
Mathematical Formulation ◽  
Model Performance ◽  
Performance Model ◽  
Electricity Production ◽  
Steam Generation ◽  
Time Step ◽  
Wide Range ◽  
Direct Steam ◽  
Solar Field

This paper presents the technical formulation and demonstrated model performance results of a new direct-steam-generation (DSG) model in NREL’s System Advisor Model (SAM). The model predicts the annual electricity production of a wide range of system configurations within the DSG Linear Fresnel technology by modeling hourly performance of the plant in detail. The quasi-steady-state formulation allows users to investigate energy and mass flows, operating temperatures, and pressure drops for geometries and solar field configurations of interest. The model includes tools for heat loss calculation using either empirical polynomial heat loss curves as a function of steam temperature, ambient temperature, and wind velocity, or a detailed evacuated tube receiver heat loss model. Thermal losses are evaluated using a computationally efficient nodal approach, where the solar field and headers are discretized into multiple nodes where heat losses, thermal inertia, steam conditions (including pressure, temperature, enthalpy, etc.) are individually evaluated during each time step of the simulation. This paper discusses the mathematical formulation for the solar field model and describes how the solar field is integrated with the other subsystem models, including the power cycle and optional auxiliary fossil system. Model results are also presented to demonstrate plant behavior in the various operating modes.

scholarly journals Using Split Samples to Improve Inference on Causal Effects

2017 ◽  
Vol 25 (4) ◽  
pp. 465-482 ◽  
Author(s):  
Marcel Fafchamps ◽  
Julien Labonne
Keyword(s):  
Sample Size ◽  
Empirical Research ◽  
Training Sample ◽  
Statistical Procedure ◽  
Third Party ◽  
Causal Effects ◽  
Testing Sample ◽  
Lower Likelihood

We discuss a statistical procedure to carry out empirical research that combines recent insights about preanalysis plans (PAPs) and replication. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the training sample and are able to incorporate feedback from both colleagues, editors, and referees. Once the paper is accepted for publication the method is applied to the testing sample and it is those results that are published. Simulations indicate that, under empirically relevant settings, the proposed method delivers more power than a PAP. The effect mostly operates through a lower likelihood that relevant hypotheses are left untested. The method appears better suited for exploratory analyses where there is significant uncertainty about the outcomes of interest. We do not recommend using the method in situations where the treatment are very costly and thus the available sample size is limited. An interpretation of the method is that it allows researchers to perform direct replication of their work. We also discuss a number of practical issues about the method’s feasibility and implementation.

Two-dimensional non-Gaussian autoregressive model order determination

2002 ◽  
Vol 9 (12) ◽  
pp. 426-428 ◽  
Author(s):  
S. Rital ◽  
A. Meziane ◽  
M. Rziza ◽  
D. Aboutajdine
Keyword(s):  
Autoregressive Model ◽  
Two Dimensional ◽  
Model Order ◽  
Non Gaussian ◽  
Model Order Determination ◽  
Order Determination

scholarly journals Time Series Modeling on Monthly Data of Tourist Arrivals in Nepal: An Alternative Approach

2017 ◽  
Vol 1 ◽  
pp. 41-54 ◽  
Author(s):  
Amrit Subedi
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Time Series Data ◽  
Polynomial Function ◽  
Seasonal Fluctuation ◽  
Ar Model ◽  
Series Data ◽  
Monthly Data ◽  
Annual Trend ◽  
Alternative Approach

Background: There are various approaches of modeling on time series data. Most of the studies conducted regarding time series data are based on annual trend whereas very few concerned with data having monthly fluctuation. The data of tourist arrivals is an example of time series data with monthly fluctuation which reveals that there is higher number of tourist arrivals in some months/seasons whereas others have less number. Starting from January, it makes a complete cycle in every 12 months with 3 bends indicating that it can be captured by biquadratic function.Objective: To provide an alternative approach of modeling i.e. combination of Autoregressive model with polynomial (biquadratic) function on time series data with monthly/seasonal fluctuation and compare its adequacy with widely used cyclic autoregressive model i.e. AR (12).Materials and Methods: This study is based on monthly data of tourist arrivals in Nepal. Firstly, usual time series model AR (12) has been adopted and an alternative approach of modeling has been attempted combining AR and biquadratic function. The first part of the model i.e. AR represents annual trend whereas biquadratic part does for monthly fluctuation.Results: The fitted cyclic autoregressive model on monthly data of tourist arrivals is Est. Ym = 3614.33 + 0.9509Ym-12, (R2=0.80); Est. Ym indicates predicted tourist arrivals for mth month and Ym-12 indicates observed tourist arrivals in (m-12)th month and the combined model of AR and biquadratic function is Est. Yt(m) = -46464.6 + 1.000Yt-1 + 52911.56m - 17177m2 + 2043.95m3 - 79.43m4, (R2=0.78); Est. Yt(m) indicates predicted tourist arrivals for mth month of tth year and Yt-1 indicates average tourist arrivals in (t-1)th year. The AR model combined with polynomial function reveals normal and homoscedastic residuals more accurately compared to first one.Conclusion: The use of polynomial function combined with autoregressive model can be useful for time series data having seasonal fluctuation. It can be an alternative approach for picking up a good model for such type of data. Nepalese Journal of Statistics, 2017,  Vol. 1, 41-54

scholarly journals Explaining Bad Forecasts in Global Time Series Models

2021 ◽  
Vol 11 (19) ◽  
pp. 9243
Author(s):  
Jože Rožanec ◽  
Elena Trajkova ◽  
Klemen Kenda ◽  
Blaž Fortuna ◽  
Dunja Mladenić
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Time Series Forecasting ◽  
Modular Architecture ◽  
Detection Algorithms ◽  
Training Samples ◽  
Forecasting Performance ◽  
Global Time ◽  
Real World Datasets ◽  
Value Changes

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and explainable artificial intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand how value changes in the feature vector can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.

Global BROOK90: validation, uncertainties, current progress and future outline.

2021 ◽  
Author(s):  
Ivan Vorobevskii ◽  
Rico Kronenberg
Keyword(s):  
Water Balance ◽  
Model Performance ◽  
Low Flow ◽  
Easy Access ◽  
Climate Projections ◽  
Seasonal Forecasts ◽  
Water Balance Components ◽  
Discharge Data ◽  
Small Catchment ◽  
Wide Range

<p>‘Just drop a catchment and receive reasonable model output’ – still stays as motto and main idea of the ‘Global BROOK90’ project. The open-source R-package is build-up on global land cover, soil, topographical, meteorological datasets and the lumped hydrological model as a core to simulate water balance components on HRU scale all over the world in an automatic mode. First introduced in EGU2020 and followed by GitHub code release including an publication of methodology with few examples we want to continue with the insights on the current state and highlight the future steps of the project.</p><p>A global validation of discharge and evapotranspiration components of the model showed promising results. We used 190 small (median size of 64 km<sup>2</sup>) catchments and FLUXNET data which represent a wide range of relief, vegetation and soil types within various climate zones. The model performance was evaluated with NSE, KGE, KGESS and MAE. In more than 75 % of the cases the framework performed better than the mean of the observed discharge. On a temporal scale the performance is significantly better on a monthly vs daily scale. Cluster analysis revealed that some of the site characteristics have a significant influence on the performance. Additionally, it was found that Global BROOK90 outperforms GloFAS ERA5 discharge reanalysis (for the category with smallest catchments).</p><p>A cross-combination of three different BROOK90 setups and three forcing datasets was set up to reveal uncertainties of the Global BROOK90 package using a small catchment in Germany as a case study. Going from local to regional and finally global scale we compared mixtures of model parameterization schemes (original calibrated BROOK90, EXTRUSO and Global BROOK90) and meteorological datasets (local gauges, RaKlida and ERA5). Besides high model performances for a local dataset plus a calibrated model and weaker results for ERA5 and the Global BROOK90, it was found that the ERA5 dataset is still able to provide good results when combined with a regional and local parameterization. On the other side, the combination of a global parameterization with local and regional forcings gives still adequate, but much worse results. Furthermore, a hydrograph separation revealed that the Global BROOK90 parameterization as well as ERA5 discharge data perform weaker especially within low flow periods.</p><p>Currently, some new features are added to the original package. First, with the recent release of the ERA5 extension, historical simulations with the package now are expanded to 1950-2021 period. Additionally, an alternative climate reanalysis dataset is included in the framework (Merra-2, 0.5x0.625-degree spatial resolution, starting from 1980). A preliminary validation shows insignificant differences between both meteorological datasets with respect to the discharge based model performance.</p><p>Further upgrades of the framework will include the following core milestones: recognition of forecast and climate projections and parameter optimization features. In the nearest future we plan to utilize full power of the Climate Data Store for easy access to seasonal forecasts (i.e. ECMWF, DWD, NCEP) as well as climate projections (CMIP5) to extend the package’s scope to predict near and far future water balance components.</p>

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