Multiple time period imputation technique for multiple missing traffic variables: nonparametric regression approach

2012 ◽  
Vol 39 (4) ◽  
pp. 448-459 ◽  
Author(s):  
Hyunho Chang ◽  
Dongjoo Park ◽  
Younginn Lee ◽  
Byoungjo Yoon
Keyword(s):  
Real World ◽  
Moving Average ◽  
Real Data ◽  
Multiple Time ◽  
Real World Data ◽  
Time Period ◽  
Proposed Model ◽  
Auto Regressive ◽  
Missing Variables ◽  
The Relationship

The objective of this study is to introduce an effective and practical model, based on non-parametric regression, to instantaneously estimate multivariate imputations replacing multiple missing variables during multiple time periods. The developed model was essentially designed for system-oriented, real-world applications. In an empirical study with real-world data, the proposed model, on the whole, outperformed the seasonal auto-regressive integrated moving average (ARIMA). The analysis of the results indicates that the introduced model was more applicable to multivariate imputation during multiple time intervals than that of ARIMA. In addition, it was revealed that ARIMA could somewhat deform the relationship between the volume (q) and speed (s), whereas the developed model reproduced the q–s relationship more similarly than ARIMA. Moreover, the proposed model is very simple and does not require system operators to input or recalibrate any external parameters because it was developed for applications of real data management systems.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

The failure of certain fractional calculus operators in two physical models

2019 ◽  
Vol 22 (2) ◽  
pp. 255-270 ◽  
Author(s):  
Manuel D. Ortigueira ◽  
Valeriy Martynyuk ◽  
Mykola Fedula ◽  
J. Tenreiro Machado
Keyword(s):  
Fractional Calculus ◽  
Human Body ◽  
Real World ◽  
Fractional Derivatives ◽  
Electrical Impedance ◽  
Real Data ◽  
Physical Models ◽  
Data Sets ◽  
Real World Data ◽  
Fractional Calculus Operators

Abstract The ability of the so-called Caputo-Fabrizio (CF) and Atangana-Baleanu (AB) operators to create suitable models for real data is tested with real world data. Two alternative models based on the CF and AB operators are assessed and compared with known models for data sets obtained from electrochemical capacitors and the human body electrical impedance. The results show that the CF and AB descriptions perform poorly when compared with the classical fractional derivatives.

scholarly journals Fractional Dynamics in Soccer Leagues

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 356 ◽  
Author(s):  
António M. Lopes ◽  
Jose A. Tenreiro Machado
Keyword(s):  
Mathematical Modeling ◽  
Fractional Calculus ◽  
Real World ◽  
Complex Dynamics ◽  
Statistical Information ◽  
Fractional Dynamics ◽  
Entropy Analysis ◽  
Real World Data ◽  
Proposed Model ◽  
Soccer Teams

This paper addresses the dynamics of four European soccer teams over the season 2018–2019. The modeling perspective adopts the concepts of fractional calculus and power law. The proposed model embeds implicitly details such as the behavior of players and coaches, strategical and tactical maneuvers during the matches, errors of referees and a multitude of other effects. The scale of observation focuses the teams’ behavior at each round. Two approaches are considered, namely the evaluation of the team progress along the league by a variety of heuristic models fitting real-world data, and the analysis of statistical information by means of entropy. The best models are also adopted for predicting the future results and their performance compared with the real outcome. The computational and mathematical modeling lead to results that are analyzed and interpreted in the light of fractional dynamics. The emergence of patterns both with the heuristic modeling and the entropy analysis highlight similarities in different national leagues and point towards some underlying complex dynamics.

VP07 Collaboratively Modelling The Impact Of Interventions Retrospectively

2017 ◽  
Vol 33 (S1) ◽  
pp. 149-149
Author(s):  
Gordon Bache ◽  
Sukh Tatla ◽  
Deborah Simpson
Keyword(s):  
Real World ◽  
Real Data ◽  
System Level ◽  
Local Data ◽  
Real World Data ◽  
Total Saving ◽  
Retrospective Assessment ◽  
Administration Time ◽  
The Individual ◽  
The Impact

INTRODUCTION:A conventional approach to communicating value is to model the budget impact of a medicine and the associated formulations in which it is available to be prescribed. However, such an approach does not demonstrate the actual realization of the proposed impact. This abstract outlines an approach to presenting retrospective data back to healthcare professionals (HCP) that blends assumptions and real-world data. For illustrative purposes, we present the results of an application of the model for subcutaneously delivered trastuzumab in an anonymized trust in Yorkshire and Humber.METHODS:The authors developed a model that examined one calendar year (from April 2014) of redistributed sales data for both the intravenous and subcutaneous formulations of trastuzumab for every National Health Service (NHS) trust in England. A series of baseline assumptions (1) were used to model the resource impact of different formulations such as chair time, HCP time, pharmacy preparation time, consumables, wastage, and other considerations. Impacts were estimated at the individual attendance level and scaled to the caseload. These baseline assumptions could then be overwritten by the individual trust using local data.RESULTS:The site delivered approximately 985 doses of subcutaneous trastuzumab over a period of 12 months from April 2014, which represented about 76 percent of the total number of doses delivered. Chair time is estimated to have reduced by 22 minutes per attendance, resulting in a total saving of 361hours. HCP administration time is estimated to have reduced by 23 minutes per attendance, resulting in a total saving of 378 hours based on changing 985 IV doses to SC therapy.CONCLUSIONS:Blending real data and assumptions to provide a retrospective assessment of actual benefits realized back to HCPs is a powerful tool for demonstrating real-world value at both an individual trust and system level.

scholarly journals OPTIMISING THE SMOOTHNESS AND ACCURACY OF MOVING AVERAGE FOR STOCK PRICE DATA

2018 ◽  
Vol 24 (3) ◽  
pp. 984-1003 ◽  
Author(s):  
Aistis RAUDYS ◽  
Židrina PABARŠKAITĖ
Keyword(s):  
Time Series ◽  
Real World ◽  
Stock Price ◽  
Weather Forecasting ◽  
Moving Average ◽  
New Method ◽  
Traffic Prediction ◽  
Real World Data ◽  
Price Series ◽  
Unseen Data

Smoothing time series allows removing noise. Moving averages are used in finance to smooth stock price series and forecast trend direction. We propose optimised custom moving average that is the most suitable for stock time series smoothing. Suitability criteria are defined by smoothness and accuracy. Previous research focused only on one of the two criteria in isolation. We define this as multi-criteria Pareto optimisation problem and compare the proposed method to the five most popular moving average methods on synthetic and real world stock data. The comparison was performed using unseen data. The new method outperforms other methods in 99.5% of cases on synthetic and in 91% on real world data. The method allows better time series smoothing with the same level of accuracy as traditional methods, or better accuracy with the same smoothness. Weights optimised on one stock are very similar to weights optimised for any other stock and can be used interchangeably. Traders can use the new method to detect trends earlier and increase the profitability of their strategies. The concept is also applicable to sensors, weather forecasting, and traffic prediction where both the smoothness and accuracy of the filtered signal are important.

scholarly journals Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

2022 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Arunkumar P. M. ◽  
Lakshmana Kumar Ramasamy ◽  
Amala Jayanthi M.
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Auto Regressive ◽  
Important Research Area

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

scholarly journals Impact of COVID-19 on selected criminal activities in Dhaka, Bangladesh

2020 ◽  
Author(s):  
Sungida Rashid
Keyword(s):  
Drug Trafficking ◽  
Moving Average ◽  
Illegal Drug ◽  
Future Research ◽  
Crime Trends ◽  
Substantial Impact ◽  
Moving Average Model ◽  
Auto Regressive ◽  
Crime Types ◽  
The Relationship

Abstract The COVID-19 pandemic has made a substantial impact on the historical criminal trend of the world. Using Dhaka Metropolitan Police (DMP) recorded open crime data of the total number of arrests, this article aims to understand how the frequency of selected crime trends has changed during the pandemic in Dhaka city, the capital of Bangladesh. Time-series forecasting models ARIMA (Auto Regressive Moving Average Model) are used to forecast the expected frequency of arrests in different crime types in 2020 in the absence of the pandemic. Forecasting techniques are applied to estimate six-month-ahead forecasts of the total number of arrests of arms dealing, vehicle theft, and illegal drug trafficking. The actual and predicted numbers of total arrests for vehicle thefts are decreased during pandemic while actual arrests in illegal drug trafficking show a steep upward trend- around 75% more than that of the expected frequencies. Estimated results are used to recognize scopes and suggestions for future research on the relationship between crimes and pandemic.

Malaria Disease Distribution in Sudan Using Time Series ARIMA Model

2015 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Mohammed Ibrahim Musa
Keyword(s):  
African Country ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Climate Factors ◽  
Training Set ◽  
The World ◽  
Auto Regressive ◽  
Sub Saharan ◽  
The Relationship

<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

scholarly journals A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 26-46 ◽  
Author(s):  
Radhakrishnan Angamuthu Chinnathambi ◽  
Anupam Mukherjee ◽  
Mitch Campion ◽  
Hossein Salehfar ◽  
Timothy Hansen ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Moving Average ◽  
Percentage Error ◽  
Spot Prices ◽  
Multi Stage ◽  
Proposed Model ◽  
Forecasting Method ◽  
Auto Regressive ◽  
Stage 1

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

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