scholarly journals Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables

2021 ◽  
Author(s):  
Theresa Maria Rausch ◽  
Tobias Albrecht ◽  
Daniel Baier
Keyword(s):  
Time Series ◽  
Call Center ◽  
Contextual Information ◽  
Waiting Times ◽  
Dynamic Environment ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Predictor Variables ◽  
Harmonic Regression ◽  
E Mail

AbstractModern call centers require precise forecasts of call and e-mail arrivals to optimize staffing decisions and to ensure high customer satisfaction through short waiting times and the availability of qualified agents. In the dynamic environment of multi-channel customer contact, organizational decision-makers often rely on robust but simplistic forecasting methods. Although forecasting literature indicates that incorporating additional information into time series predictions adds value by improving model performance, extant research in the call center domain barely considers the potential of sophisticated multivariate models. Hence, with an extended dynamic harmonic regression (DHR) approach, this study proposes a new reliable method for call center arrivals’ forecasting that is able to capture the dynamics of a time series and to include contextual information in form of predictor variables. The study evaluates the predictive potential of the approach on the call and e-mail arrival series of a leading German online retailer comprising 174 weeks of data. The analysis involves time series cross-validation with an expanding rolling window over 52 weeks and comprises established time series as well as machine learning models as benchmarks. The multivariate DHR model outperforms the compared models with regard to forecast accuracy for a broad spectrum of lead times. This study further gives contextual insights into the selection and optimal implementation of marketing-relevant predictor variables such as catalog releases, mail as well as postal reminders, or billing cycles.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

scholarly journals Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

2019 ◽  
Vol 23 (10) ◽  
pp. 4323-4331 ◽  
Author(s):  
Wouter J. M. Knoben ◽  
Jim E. Freer ◽  
Ross A. Woods
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Ad Hoc ◽  
Model Performance ◽  
Technical Note ◽  
Mean Flow ◽  
Model Adequacy ◽  
Robust Model ◽  
The Mean ◽  
Adequacy Assessment

Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

scholarly journals The Role of Assumptions in Ohlson Model Performance: Lessons for Improving Equity-Value Modeling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 513
Author(s):  
Olga Fullana ◽  
Mariano González ◽  
David Toscano
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Listed Firms ◽  
Valuation Model ◽  
Residual Income Valuation ◽  
Equity Value ◽  
Short Run ◽  
Time Series Approach ◽  
Linear Information

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

scholarly journals Hospital acquired pressure injury prediction in surgical critical care patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jenny Alderden ◽  
Kathryn P. Drake ◽  
Andrew Wilson ◽  
Jonathan Dimas ◽  
Mollie R. Cummins ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Performance ◽  
Routine Care ◽  
Predictor Variables ◽  
Classification Algorithms ◽  
Data Set ◽  
Icu Patients ◽  
Pressure Injury ◽  
Surgical Icu ◽  
Hospital Acquired

Abstract Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Tracking Animals in a Dynamic Environment: Remote Sensing Image Time Series

2014 ◽  
pp. 95-114
Author(s):  
Mathieu Basille ◽  
Ferdinando Urbano ◽  
Pierre Racine ◽  
Valerio Capecchi ◽  
Francesca Cagnacci
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Dynamic Environment ◽  
Remote Sensing Image

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.

scholarly journals Evaluating skill of BMKG wave model forecast (Wavewatch-3) with observation data in Indian Ocean (5 – 31 December 2017).

2021 ◽  
Vol 893 (1) ◽  
pp. 012058
Author(s):  
R Kurniawan ◽  
H Harsa ◽  
A Ramdhani ◽  
W Fitria ◽  
D Rahmawati ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Wave Height ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Wave Model ◽  
Ocean Waves ◽  
Observation Data ◽  
Operating Characteristics ◽  
Wave Forecast ◽  
Good For

Abstract Providing Maritime meteorological forecasts (including ocean wave information) is one of BMKG duties. Currently, BMKG employs Wavewatch-3 (WW3) model to forecast ocean waves in Indonesia. Evaluating the wave forecasts is very important to improve the forecasts skill. This paper presents the evaluation of 7-days ahead BMKG’s wave forecast. The evaluation was performed by comparing wave data observation and BMKG wave forecast. The observation data were obtained from RV Mirai 1708 cruise on December 5th to 31st 2017 at the Indian Ocean around 04°14'S and 101°31'E. Some statistical properties and Relative Operating Characteristics (ROC) curve were utilized to assess the model performance. The evaluation processes were carried out on model’s parameters: Significant Wave Height (Hs) and Wind surface for each 7-days forecast started from 00 UTC. The comparation results show that, in average, WW3 forecasts are over-estimate the wave height than that of the observation. The forecast skills determined from the correlation and ROC curves are good for the first- and second-day forecast, while the third until seventh day decrease to fair. This phenomenon is suspected to be caused by the wind data characteristics provided by the Global Forecasts System (GFS) as the input of the model. Nevertheless, although statistical correlation is good for up to 2 days forecast, the average value of Root Mean Square Error (RMSE), absolute bias, and relative error are high. In general, this verifies the overestimate results of the model output and should be taken into consideration to improve BMKG’s wave model performance and forecast accuracy.

Creating the collective mind through virtual mirroring based learning

2018 ◽  
Vol 32 (3) ◽  
pp. 4-7
Author(s):  
Peter Gloor ◽  
Kai Fischbach ◽  
Julia Gluesing ◽  
Ken Riopelle ◽  
Detlef Schoder
Keyword(s):  
Call Center ◽  
Quantum Physics ◽  
Content Type ◽  
Guiding Principle ◽  
Individual Privacy ◽  
Sales Forecasts ◽  
Geographical Maps ◽  
And Performance ◽  
The Individual ◽  
E Mail

Purpose The purpose of this paper is to show that virtual mirroring-based learning allows members of an organization to see how they communicate with others in a visual way, by applying principles of “social quantum physics” (empathy, entanglement, reflect, reboot), to become better communicators and build a shared “DNA” within their organization. Design/methodology/approach E-mail based social network analysis creates virtual maps of communication – social landscapes – of organizations, similar to Google Maps, which creates geographical maps of a person’s surroundings. Findings Applying virtual mirroring-based learning at various mulitnational firms has significantly increased their organizational efficiency and performance, for instance increasing customer satisfaction by 18 per cent in a large services organization, increasing retention, making sales forecasts, and improving call center employee satisfaction. Research limitations/implications To address concerns of individual privacy, the guiding principle is to give individual information to the individual and provide aggregated anonymized information to management. Originality/value Virtual mirroring-based learning offers a unique way of creating collective awareness within an organization by empowering the individual to take corrective action aligned with collective action, and improves their own communication behavior through analyzing and visualizing their e-mail archive in novel ways, while giving strategic insight to management and improving organizational culture.

ENHANCING QUALITY OF IT SERVICES DELIVERY USING ENTERPRISE CROWDSOURCING

2013 ◽  
Vol 22 (03) ◽  
pp. 1340001 ◽  
Author(s):  
MAJA VUKOVIC ◽  
ARJUN NATARAJAN
Keyword(s):  
Call Center ◽  
Large Scale ◽  
Services Quality ◽  
It Outsourcing ◽  
Human Knowledge ◽  
It Services ◽  
It Infrastructure ◽  
Services Delivery ◽  
E Mail

IT outsourcing companies have adopted global delivery model, where IT services, such as backup management, are supplied out of multiple locations worldwide, based on the skill and cost of IT delivery staff, such as system administrators (SAs) and call center agents. Managing IT services quality requires insights obtained by extracting large volumes of tacit knowledge about processes, products and people, which is in collective possession of experts. Current practices to discovering this distributed and unstructured knowledge are semi-automated. Often they involve manual data collection using spreadsheets and tracking of exerts through e-mail. As such they fail to scale and provide accurate insights on demand, such as IT infrastructure snapshots. We present an enterprise crowdsourcing service that enables harnessing of human knowledge to derive quality insights in IT services. Our approach automates knowledge and knowledge owner discovery, and is based on the concept of distributed questionnaires. Experts can breakdown the knowledge requests into multiple parts and engage their networks to co-create the content. We discuss the effectiveness of our approach for knowledge discovery in the context of large-scale, on-going business activities in IT outsourcing organization, which collectively engaged over 2500 experts globally.

scholarly journals The shift in seasonality of legionellosis in the USA

2018 ◽  
Vol 146 (14) ◽  
pp. 1824-1833 ◽  
Author(s):  
T. M. Alarcon Falconi ◽  
M. S. Cruz ◽  
E. N. Naumova
Keyword(s):  
Time Series ◽  
Time Interval ◽  
Significant Shift ◽  
The Public ◽  
Before And After ◽  
Related Hospitalisation ◽  
Control And Prevention ◽  
The Usa ◽  
Harmonic Regression ◽  
National Databases

AbstractAccording to the Centers for Disease Control and Prevention (CDC), from 2000 to 2014, reported cases of legionellosis per 100 000 population increased by 300% in the USA, although reports on disease seasonality are inconsistent. Using two national databases, we assessed seasonal patterns of legionellosis in the USA. We created a monthly time series from 1993 to 2015 of reported cases of legionellosis from the CDC, and from 1997 to 2006 of medical claims of legionellosis-related hospitalisation in older adults from the Centers for Medicaid and Medicare Services (CMS). We split the study time interval into two segments (before and after 2003), and applied a Poisson harmonic regression model to each dataset and each segment. The time series of monthly counts exhibited a significant shift of seasonal peaks from mid-September (9.676 ± 0.164 months) before 2003 to mid-August (8.452 ± 0.042 months) after 2003, along with an alarming increase in the amplitude of seasonal peaks in both CDC and CMS data. The lowest monthly reported cases of legionellosis in 2015 (281) exceed the maximum value reported before 2003 (206). We also observed a discrepancy between CDC and CMS data, suggesting that not all cases of legionellosis diagnosed by hospital-based laboratories were reported to the CDC. Improved reporting of legionellosis is required to better inform the public and organise disease prevention.

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