Optimum prediction and forecasting of wheat demand in Iran

Subject. In this article we analyze how machinery, science and technologies influence the sociocultural environment that engenders the teacher's paradigm of values and views of life. Objectives. We herein outline guidance to predict the way teachers' views of life might evolve in corresponding sociocultural periods more precisely. The article analyzes making more precise forecasts of oncoming economic crises, which will cause some changes in teachers' mindset. Methods. The study involves learning methodologies, methods of prediction and forecasting, including foresight. Results. We propose and analyze the theory holding that the human civilization passes cycles during its sociocultural development in terms of a new set of values in contemporary teachers' views of life. The article sets forth our recommendations on innovation-driven views of life, mindset and thinking and, consequently, the development of intellectual qualities, knowledge, skills, cognitive activity, positive motivation to the professional activity of a teacher and alumni during more elevated periods, which requires to more precisely predict the way teachers’ mindset may change in certain sociocultural periods. Conclusions and Relevance. As the human civilization enters the innovation-driven sociocultural period, teachers and social relationships should demonstrate more innovative and environmentally-friendly attitudes and views of life.

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Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽

10.3390/w13121633 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1633

Author(s):

Elena-Simona Apostol ◽

Ciprian-Octavian Truică ◽

Florin Pop ◽

Christian Esposito

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Change Point ◽

Time Series Data ◽

Multivariate Time Series ◽

Change Point Detection ◽

Change Points ◽

Series Data ◽

Prediction And Forecasting ◽

Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

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Prediction and Forecasting

Encyclopedia of Statistical Sciences ◽

10.1002/0471667196.ess2046.pub2 ◽

2006 ◽

Author(s):

Johannes Ledolter

Keyword(s):

Prediction And Forecasting

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Advancing Epidemic Prediction and Forecasting: A New US Government Initiative

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v7i1.5677 ◽

2015 ◽

Vol 7 (1) ◽

Cited By ~ 14

Author(s):

Jean-Paul Chretien ◽

David Swedlow ◽

Irene Eckstrand ◽

Dylan George ◽

Michael Johansson ◽

...

Keyword(s):

Working Group ◽

Science And Technology ◽

Group Leaders ◽

National Science ◽

Us Government ◽

Epidemic Prediction ◽

Prediction And Forecasting ◽

Government Initiative

The National Science and Technology Council, within the Executive Office of the President, established the Pandemic Prediction and Forecasting Science and Technology Working Group in 2013 to advance US Government epidemic prediction and forecasting capabilities. Working Group leaders will provide an overview of activities, and seek feedback on the Working Group direction from the ISDS community.

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A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions

H2Open Journal ◽

10.2166/h2oj.2019.022 ◽

2019 ◽

Vol 2 (1) ◽

pp. 25-44 ◽

Cited By ~ 2

Author(s):

S. Mohanasundaram ◽

G. Suresh Kumar ◽

Balaji Narasimhan

Keyword(s):

Time Series ◽

Groundwater Level ◽

Moving Average ◽

Seasonal Component ◽

Univariate Time Series ◽

Level Data ◽

Proposed Model ◽

Interval Approach ◽

Prediction And Forecasting ◽

Observation Wells

Abstract Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality pattern. Alternatively, deseasonalized autoregressive and moving average models (Ds-ARMA) can be modeled with deseasonalized groundwater level signals in which the seasonal component is estimated and removed from the raw groundwater level signals. The seasonal component is traditionally estimated by calculating long-term averaging values of the corresponding months in the year. This traditional way of estimating seasonal component may not be appropriate for non-stationary groundwater level signals. Thus, in this study, an improved way of estimating the seasonal component by adopting a 13-month moving average trend and corresponding confidence interval approach has been attempted. To test the proposed approach, two representative observation wells from Adyar basin, India were modeled by both traditional and proposed methods. It was observed from this study that the proposed model prediction performance was better than the traditional model's performance with R2 values of 0.82 and 0.93 for the corresponding wells' groundwater level data.

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Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM)

Jurnal Komtika ◽

10.31603/komtika.v5i1.5139 ◽

2021 ◽

Vol 5 (1) ◽

pp. 10-18

Author(s):

Herlawati Herlawati ◽

Fata Nidaul Khasanah ◽

Prima Dina Atika ◽

Rafika Sari ◽

Rahmadya Trias Handayanto

Keyword(s):

Land Use ◽

Suburban Areas ◽

Green Areas ◽

Accuracy Result ◽

Prediction And Forecasting ◽

Business As Usual ◽

The City ◽

Land Types ◽

Regional Types

Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry

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Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

Bangladesh Journal of Medical Science ◽

10.3329/bjms.v19i0.47611 ◽

2020 ◽

pp. 6-20

Author(s):

Handan Ankaralı ◽

Nadire Erarslan ◽

Özge Pasin ◽

Abu Kholdun Al Mahmood

Keyword(s):

Time Series ◽

Medical Science ◽

Exponential Smoothing ◽

Seasonal Effects ◽

Time Series Models ◽

Double Exponential ◽

Wide Range ◽

Simple Exponential Smoothing ◽

Double Exponential Smoothing ◽

Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

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