Erratum for “Purdue Index for Construction Analytics: Prediction and Forecasting Model Development” by Arkaprabha Bhattacharyya, Soojin Yoon, Theodore J. Weidner, and Makarand Hastak

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

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Fast and Accurate Influenza Forecasting in the United States with Inferno

10.1101/2021.01.06.425546 ◽

2021 ◽

Author(s):

Dave Osthus

Keyword(s):

Situational Awareness ◽

Model Development ◽

Predictive Performance ◽

The United States ◽

Primary Objective ◽

Forecasting Model ◽

Accurate Information ◽

Disease Forecasting ◽

Minimal Impact ◽

Forecasting Models

AbstractInfectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development, improvement, and scalability. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in both the national and state challenges, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. A future consideration for forecasting competitions like FluSight will be how to encourage improvements to secondarily important properties of forecasting models, such as runtime, generalizability, and interpretability.

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A systematic feature selection procedure for short-term data-driven building energy forecasting model development

Energy and Buildings ◽

10.1016/j.enbuild.2018.11.010 ◽

2019 ◽

Vol 183 ◽

pp. 428-442 ◽

Cited By ~ 19

Author(s):

Liang Zhang ◽

Jin Wen

Keyword(s):

Feature Selection ◽

Model Development ◽

Selection Procedure ◽

Building Energy ◽

Data Driven ◽

Forecasting Model ◽

Short Term ◽

Energy Forecasting ◽

Systematic Feature ◽

Term Data

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Multilayer Stock Forecasting Model Using Fuzzy Time Series

The Scientific World JOURNAL ◽

10.1155/2014/610594 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Hossein Javedani Sadaei ◽

Muhammad Hisyam Lee

Keyword(s):

Stock Market ◽

Model Development ◽

Single Layer ◽

Fuzzy Time Series ◽

Stock Index ◽

Forecasting Model ◽

Stock Market Forecasting ◽

Huge Data ◽

Dow Jones Industrial Average ◽

Systematic Framework

After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.

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