scholarly journals Prediction of water consumption by consumer categories: a case study

2020 ◽  
Vol 42 ◽  
pp. e110
Author(s):  
Jorge Alberto Achcar ◽  
Marcos Valerio Araujo ◽  
Claudio Luis Piratelli ◽  
Ricardo Puziol de Oliveira
Keyword(s):  
Water Consumption ◽  
Bayesian Model ◽  
Moving Average ◽  
Random Effect ◽  
Hierarchical Bayesian Analysis ◽  
Advantages And Disadvantages ◽  
Proposed Model ◽  
Consumption Data ◽  
Moving Average Models ◽  
Water Supply Company

This study introduces a new Bayesian model for predicting water consumption in a medium-sized municipality in the State of São Paulo, Brazil. For the study, a stratified random sample of water consumption for consumers in different consumer categories (residential, industrial, public and commercial) is selected for 55 monthly consecutive measurements of water consumption and the proposed model is compared with some usual existing time series models (moving average models and ARIMA models) commonly used in forecasts. The Bayesian model for the consumption data assumes the presence of a random effect that captures the possible dependence between the monthly consumption for the different categories. A hierarchical Bayesian analysis is done using MCMC (Markov Chain Monte Carlo) methods to generate samples of the joint posterior distribution of interest. A detailed discussion of the results obtained is presented, showing the advantages and disadvantages of each model proposed in terms of feasibility for the municipality's water supply company. The results of this study can be generalized to water consumption data for any municipality.

scholarly journals ARIMA-M: A New Model for Daily Water Consumption Prediction Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 760 ◽  
Author(s):  
Hongyan Du ◽  
Zhihua Zhao ◽  
Huifeng Xue
Keyword(s):  
Markov Chain ◽  
Water Consumption ◽  
Prediction Error ◽  
Moving Average ◽  
Arima Model ◽  
Regional Environment ◽  
Consumption Data ◽  
Daily Water ◽  
Consumption Prediction ◽  
Daily Water Consumption

Water resource is considered as a significant factor in the development of regional environment and society. Water consumption prediction can provide an important decision basis for the regional water supply scheduling optimizations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model was proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points were used to verify the effectiveness of the model, and the future water consumption was predicted in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.

scholarly journals Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models

2020 ◽  
pp. 135-146
Author(s):  
Olumide Sunday Adesina ◽  
Samson Adeniyi Onanaye ◽  
Dorcas Okewole ◽  
Amanze C. Egere
Keyword(s):  
Moving Average ◽  
Arima Models ◽  
Rate Of Spread ◽  
Arfima Models ◽  
Global Pandemic ◽  
Proposed Model ◽  
Moving Average Models ◽  
The World ◽  
Arfima Model

The emergence of global pandemic known as COVID-19 has impacted significantly on human lives and measures have been taken by government all over the world to minimize the rate of spread of the virus, one of which is by enforcing lockdown. In this study, Autoregressive fractionally integrated moving average (ARFIMA) Models was used to model and forecast what the daily new cases of COVID-19 would have been ten days after the lockdown was eased in Nigeria and compare to the actual new cases for the period when the lockdown was eased.  The proposed model ARFIMA model was compared with ARIMA (1, 0, 0), and ARIMA (1, 0, 1) and found to outperform the classical ARIMA models based on AIC and BIC values. The results show that the rate of spread of COVID-19 would have been significantly less if the strict lockdown had continued. ARFIMA model was further used to model what new cases of COVID-19 would be ten days ahead starting from 31st of August 2020. Therefore, this study recommends that government should further enforce measures to reduce the spread of the virus if business must continue as usual.

scholarly journals Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Peninsular Malaysia ◽  
Series Data ◽  
Proposed Model ◽  
Moving Average Models ◽  
Recorded Data ◽  
The Individual

The accuracy of the wavelet-ARIMA (WA) model in monthly fishery landing forecasting is investigated in the study. In the first part of the study, the discrete wallet transform (DWT) is used to decompose fishery landing time series data. Then ARIMA, as a powerful forecasting tool, is implemented to predict each wavelet transform subseries components independently. Finally, the prediction results of the modeled subseries components are summed to formulate an ensemble forecast for the original fishery landing series. To assess the effectiveness of this model, monthly fishery landing recorded data from East Johor and Pahang states of Peninsular Malaysia have been used as a case study. The result of the study shows that the proposed model was found to provide more accurate fishery landing series forecasts than the individual ARIMA model.

scholarly journals An algorithm for detecting intense anomalous changes in the time dependence of ionospheric parameters

2019 ◽  
Vol 43 (6) ◽  
pp. 1064-1071 ◽  
Author(s):  
N.V. Fetisova
Keyword(s):  
Weather Forecasting ◽  
Moving Average ◽  
Space Weather Forecasting ◽  
Multi Scale ◽  
Combined Application ◽  
Proposed Model ◽  
Moving Average Models ◽  
Critical Frequency Fof2 ◽  
On Line ◽  
Model Components

The paper presents a modified multicomponent model of ionospheric parameter time series. The model describes regular variations and anomalous changes of a multi-scale structure that characterize the occurrence of ionospheric irregularities. Identification of the model components is based on a combined application of the wavelet transform and autoregressive-integrated moving average models. An algorithm for analyzing ionospheric parameters has been developed on the basis of the proposed model. The algorithm allows the intensive ionospheric anomalies characterizing the occurrence of strong ionospheric storms to be detected on-line. Results of the evaluation of the algorithm performance are presented. The evaluation is performed by the example of processing and analyzing hourly and 15-minute data on the ionospheric critical frequency (foF2) during magnetic storms in 2015 – 2017. The performed estimations showed the efficiency of the algorithm and the possibility of its application for space weather forecasting.

scholarly journals Electricity consumption forecasting using DFT decomposition based hybrid ARIMA-DLSTM model

2021 ◽  
Vol 24 (2) ◽  
pp. 1107
Author(s):  
Osman Yakubu ◽  
Narendra Babu C.
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Electricity Consumption ◽  
Series Data ◽  
Linear Component ◽  
Proposed Model ◽  
Consumption Data ◽  
Linear And Nonlinear ◽  
Nonlinear Components

Forecasting electricity consumption is vital, it guides policy makers and electricity distribution companies in formulating policies to manage production and curb pilfering. Accurately forecasting electricity consumption is a challenging task. Relying on a single model to forecast electricity consumption data which comprises both linear and nonlinear components produces inaccurate results. In this paper, a hybrid model using autoregressive integrated moving average (ARIMA) and deep long short-term memory (DLSTM) model based on discrete fourier transform (DFT) decomposition is presented. Aided by its superior decomposition capability, filtering using DFT can efficiently decompose the data into linear and nonlinear components. ARIMA is employed to model the linear component, while DLSTM is applied on the nonlinear component; the two predictions are then combined to obtain the final predicted consumption. The proposed techniques are applied on the household electricity consumption data of France to obtain forecasts for one day, one week and ten days ahead consumption. The results reveal that the proposed model outperforms other benchmark models considered in this investigation as it attained lower error values. The proposed model could accurately decompose time series data without exhibiting a performance degradation, thereby enhancing prediction accuracy.

scholarly journals Asymmetric vector moving average models: estimation and testing

2021 ◽  
Author(s):  
Jan G. De Gooijer
Keyword(s):  
Moving Average ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Finite Sample ◽  
Test Statistic ◽  
Multivariate Test ◽  
Testing Procedures ◽  
Proposed Model ◽  
Moving Average Models ◽  
Global Invertibility

AbstractWe propose the class of asymmetric vector moving average (asVMA) models. The asymmetry of these models is characterized by different MA filters applied to the components of vectors of lagged positive and negative innovations. This allows for a detailed investigation of the interrelationships among past model innovations of different sign. We derive some covariance matrix properties of an asVMA model under the assumption of Gaussianity. Related to this, we investigate the global invertibility condition of the proposed model. The paper also introduces a maximum likelihood estimation procedure and a multivariate Wald-type test statistic for symmetry versus the alternative of asymmetry. The finite-sample performance of the proposed multivariate test is studied by simulation. Furthermore, we devise an exploratory test statistic based on lagged sample cross-bicovariance estimates. The estimation and testing procedures are used to uncover asymmetric effects in two US growth rates, and in three US industrial prices.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

Investigating artificial groundwater recharge to ensure the water supply to the city of Graz

2008 ◽  
Vol 3 (3) ◽  
Author(s):  
Wilhelm Tischendorf ◽  
Hans Kupfersberger ◽  
Christian Schilling ◽  
Oliver Gabriel
Keyword(s):  
Water Supply ◽  
Groundwater Recharge ◽  
Water Demand ◽  
Bulk Water ◽  
Retention Rates ◽  
Subsurface Water ◽  
Artificial Groundwater Recharge ◽  
Advantages And Disadvantages ◽  
Water Recharge ◽  
Water Supply Company

Being Austria's fourth largest water-supply company, the Grazer Stadtwerke AG., has ensured the successful water-supply of the Styrian capital with 250.000 inhabitants for many years. The average daily water demand of the area amounts to about 50,000 m3. Approximately 30 % of the total demand is covered by the bulk water supply from the Zentral Wasser Versorgung Hochschwab Süd. The waterworks Friesach and Andritz, which cover the additional 70 % of the water demand, operate by means of artificial groundwater recharge plants where horizontal filter wells serve as drawing shafts. The groundwater recharge systems serve to increase the productivity of the aquifer and to reduce the share of the infiltration from the Mur River. Protection areas have been identified to ensure that the water quality of the aquifer stay at optimal levels. The protection areas are divided into zones indicating various restrictions for usage and planning. Two respective streams serve as the source for the water recharge plants. Different infiltration systems are utilised. Each of the various artificial groundwater recharge systems displays specific advantages and disadvantages in terms of operation as well as maintenance. In order to secure a sustainable drinking water supply the recharge capacity will be increased. Within an experimental setting different mixtures of top soils are investigated with respect to infiltration and retention rates and compared to the characteristics of the existing basins. It can be shown that the current operating sand basin with more than 90% grains in the range between 0.063 and 6.3 mm represents the best combination of infiltration and retention rates. In future experiments the performance of alternative grain size distributions as well as planting the top soil will be tested. Additionally, in order to optimize the additional groundwater recharge structures the composition of the subsurface water regarding its origin is statistically analyzed.

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