Constrained NLS Method for Long-term Forecasting with Short-term Demand Data of a New Product

Seroepidemiology is a potentially powerful tool for predicting and monitoring the effectiveness of specific prevention program using studies of antibody prevalence. The availability of a certified collection of blood serum (serum bank) allows to carry out a reliable assessment of population immunity to vaccine-preventable diseases; to determine the degree of epidemiological risk of the infection spread in various areas of the country; to implement short-term and long-term forecasting of changes in the situation on topical infections; to substantiate preventive measures in the system of biosafety for defined population groups and decreed contingents; to provide information necessary for making the optimal management decisions.

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Fine-tuned Forecasting Techniques for COVID-19 Prediction in India

10.1101/2020.08.10.20167247 ◽

2020 ◽

Author(s):

Abhinav Gola ◽

Ravi Kumar Arya ◽

Animesh Animesh ◽

Ravi Dugh ◽

Zuber Khan

Keyword(s):

United States ◽

Numerical Models ◽

Research Work ◽

Short Term ◽

Long Term Forecasting ◽

Better Than ◽

Forecasting Techniques

Estimation of statistical quantities plays a cardinal role in handling of convoluted situations such as COVID-19 pandemic and forecasting the number of affected people and fatalities is a major component for such estimations. Past researches have shown that simplistic numerical models fare much better than the complex stochastic and regression-based models when predicting for countries such as India, United States and Brazil where there is no indication of a peak anytime soon. In this research work, we present two models which give most accurate results when compared with other forecasting techniques. We performed both short-term and long-term forecasting based on these models and present the results for two discrete durations.

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Effektive Zielgruppenansprache am Point-of-Sale

Der Betriebswirt: Volume 54, Issue 3 - Der Betriebswirt ◽

10.3790/dbw.55.3.21 ◽

2014 ◽

Vol 55 (3) ◽

pp. 21-26

Author(s):

Thorsten Teichert ◽

Mathias Valentin ◽

Sabrina Wauker

Keyword(s):

Six Sigma ◽

Market Research ◽

Product Market ◽

New Product ◽

Customer Segmentation ◽

Short Term ◽

Point Of Sale ◽

Sales Personnel ◽

Phase Analyse

Im vorangegangenen Beitrag in Ausgabe 2/2014 wurde die Bedeutung einer Integration von Marketing und Vertrieb für die Einführung neuer Produkte herausgestellt. So scheitern Produktinnovationen oft nicht aufgrund von Fehlern im Produktdesign oder im Markenkonzept, sondern durch suboptimale operative Markteinführung. Diese wird vom Vertrieb in erheblichem Maße beeinflusst. Daher gilt es, Marketing- und Vertriebsabteilung aufeinander abzustimmen und dem Vertrieb das nötige Rüstzeug bereitzustellen, um abstrakte Marketingkonzepte (wie Zielgruppensegmentierungen) im Alltag praktikabel umzusetzen. An einem Praxisbeispiel wird die Integration von Marktforschung und Vertrieb mit Hilfe der Six-Sigma-Methodik vorgestellt. Als Anwendungsfall dient eine Studie im Leuchtmittelmarkt. A structured Six-Sigma process is applied to align Marketing & Sales activities for the market introduction of a new product. Market research is used to establish a Management Cockpit. Measures enable customer segmentation at the PoS and guide sales personnel for targeting customers. Performance implications prove both short-term as well as long-term benefits. Keywords: unternehmensperformance, measure phase, improve phase, define phase, analyse phase

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COVID-19 Pandemic Development in Jordan—Short-Term and Long-Term Forecasting

Vaccines ◽

10.3390/vaccines9070728 ◽

2021 ◽

Vol 9 (7) ◽

pp. 728

Author(s):

Tareq Hussein ◽

Mahmoud H. Hammad ◽

Pak Lun Fung ◽

Marwan Al-Kloub ◽

Issam Odeh ◽

...

Keyword(s):

Early Stage ◽

Middle Eastern ◽

Forecast Model ◽

Third Wave ◽

Short Term ◽

Model Based ◽

Model Merging ◽

Term Forecast ◽

Long Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

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New Product Preannouncements and Shareholder Value: Don't Make Promises you Can't Keep

Journal of Marketing Research ◽

10.1509/jmkr.44.3.468 ◽

2007 ◽

Vol 44 (3) ◽

pp. 468-489 ◽

Cited By ~ 138

Author(s):

Alina Sorescu ◽

Venkatesh Shankar ◽

Tarun Kushwaha

Keyword(s):

Effective Means ◽

Shareholder Value ◽

Abnormal Returns ◽

New Product ◽

Specific Information ◽

Negative Consequences ◽

Competitive Reaction ◽

Short Term ◽

New Product Preannouncements

New product preannouncements are strategic signals that firms direct at their customers, competitors, channel members, and investors. They have been touted as effective means of deterring competitor entry, informing potential customers, and even tipping the balance of technological standard battles in favor of the preannouncing firms. However, preannouncements also carry the risks of unwanted competitive reaction and the negative consequences of undelivered promises. From a shareholder value standpoint, do the benefits outweigh the risks of preannouncing? To address this question, the authors build on agency and signaling theories to develop hypotheses about the effects of preannouncements on shareholder value, and they empirically test these hypotheses on a sample of software and hardware new product preannouncements. The findings indicate that the financial returns from preannouncements are significantly positive in the long run. The authors show that preannouncements generate positive short-term abnormal returns only for firms that offer specific information about the preannounced product. They also show that firms earn positive long-term abnormal returns after a preannouncement if they continue to update the market on the progress of the new product. Both the short-term and the long-term returns are further magnified if the reliability of the preannouncement (i.e., the credibility of the preannouncing firm) is high. The findings offer executives of preannouncing firms clear guidelines on how to manage communications in the market to extract financial value from new product preannouncements.

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Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method

Intelligent Systems ◽

10.4018/978-1-5225-5643-5.ch077 ◽

2018 ◽

pp. 1758-1772

Author(s):

Aditya R. Raikwar ◽

Rahul R. Sadawarte ◽

Rishikesh G. More ◽

Rutuja S. Gunjal ◽

Parikshit N. Mahalle ◽

...

Keyword(s):

Traffic Congestion ◽

Data Science ◽

Forecasting Model ◽

Short Term ◽

Traffic Forecasting ◽

Important Solution ◽

System Data ◽

Long Term Forecasting ◽

Access To Data

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.

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The effect of driver variables on the estimation of bivariate probability density of peak loads in long-term horizon

Journal Of Big Data ◽

10.1186/s40537-020-00404-8 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Zohreh Kaheh ◽

Morteza Shabanzadeh

Keyword(s):

Power Systems ◽

Probability Density ◽

Peak Load ◽

Point Estimation ◽

Probabilistic Forecasting ◽

Short Term ◽

Expansion Planning ◽

Long Term Forecasting ◽

Short Term Forecasting

AbstractIt is evident that developing more accurate forecasting methods is the pillar of building robust multi-energy systems (MES). In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variables in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, gross domestic product (GDP), and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.

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The Effect of Driver Variables on the Estimation of Bivariate Probability Density of Peak Loads in Long-Term Horizon

10.21203/rs.3.rs-50238/v2 ◽

2020 ◽

Author(s):

Zohreh Kaheh ◽

Morteza Shabanzadeh

Keyword(s):

Power Systems ◽

Probability Density ◽

Peak Load ◽

Point Estimation ◽

Probabilistic Forecasting ◽

Short Term ◽

Expansion Planning ◽

Long Term Forecasting ◽

Short Term Forecasting

Abstract It is evident that developing more accurate forecasting methods is the pillar of building robust multi-energy systems (MES). In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variable in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, gross domestic product (GDP), and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.

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