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Showing 1 to 11 of 11 for “"Seasonal autoregressive integrated moving average"”.

  1. Time Series Analysis of Fine Particulates Matter (PM2.5) in Chaoyang District of Beijing

    … the Box-Jenkins method to build a SARIMA model (Seasonal Autoregressive Integrated Moving Average). The results showed the PM2.5 concentration decreased from 2010 to 2012. The 24-hour PM2.5 concentration was 294μg/m³, ranged from 265.2μg/m³ to 318.5μg/m³. And the annual PM2.5 concentration was …

    unr Repository record for Time Series Analysis of Fine Particulates Matter (PM2.5) in Chaoyang District of Beijing (opens in a new tab)

  2. Preemptive variation reduction in biologic drug substance manufacturing

    … this hypothesis, a digital twin for a generic Integrated Continuous Bioreactor (ICB) operation was developed based on first principles. Additionally, soft sensors were used for the real-time estimation of biomass concentration (a critical parameter in mammalian cell culture). Combining …

    mit Repository record for Preemptive variation reduction in biologic drug substance manufacturing (opens in a new tab)

  3. SARIMA Short to Medium-Term Forecasting and Stochastic Simulation of Streamflow, Water Levels and Sediments Time Series from the HYDAT Database

    … water levels, and sediments in Canada using Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models. The methodology can account for linear trends in the time series that may result from climate and environmental changes. A Universal Canadian forecast Application using …

    ottawa-retro Repository record for SARIMA Short to Medium-Term Forecasting and Stochastic Simulation of Streamflow, Water Levels and Sediments Time Series from the HYDAT Database (opens in a new tab)

  4. From Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning Models

    … and residential water usage, focusing on how seasonal and environmental changes influence water consumption. Utilizing data from over 100,000 households across three micro-climate zones for over a five-year period, we apply statistical analysis and machine learning techniques to assess the …

    chapman Repository record for From Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning Models (opens in a new tab)

  5. Responding to traveling patients' seasonal demands for health care services in the Veterans Health Administration

    … Health care providers within VHA report large seasonal variation in the demand for services, especially in healthcare systems located in the southern U.S. that experience a large influx of "snowbirds" during the winter. Since the majority of resource allocation activities are carried out …

    mit Repository record for Responding to traveling patients' seasonal demands for health care services in the Veterans Health Administration (opens in a new tab)

  6. Detecting Irregular Energy Consumption Through Analytical Techniques

    … heavily influenced by the presence of multiple seasonalities and heteroskedasticity. Most established time series methods were developed on the assumption that the errors are<br/>homoskedastic, hence the prediction limits that are created from such established method will often fail in detecting …

    southwales Repository record for Detecting Irregular Energy Consumption Through Analytical Techniques (opens in a new tab)

  7. On Development and Performance Evaluation of Some Biosurveillance Methods

    … characteristics. The first part develops a seasonal autoregressive integrated moving average (SARIMA) based surveillance chart, and compares it with the CDC Early Aberration Reporting System (EARS) W2c method using both authentic and simulated data. After successfully removing the long-term …

    vt Repository record for On Development and Performance Evaluation of Some Biosurveillance Methods (opens in a new tab)

  8. Application of remotely sensed environmental variables for predicting malaria cases in Nkomazi municipality South Africa

    … at risk using GIS and RS, 4) to predict the seasonal and spatio-temporal variability of incidences of malaria. Results from this study indicated that space and time are key factors in the epidemiology of malaria, to determine spatial and temporal windows of opportunities for elimination …

    pretoria Repository record for Application of remotely sensed environmental variables for predicting malaria cases in Nkomazi municipality South Africa (opens in a new tab)

  9. Space-time statistical analysis of malaria morbidity incidence cases in Ghana: A geostatistical modelling approach

    … geostatistical space-time models and time series seasonal autoregressive integrated moving average (SARIMA) predictive models have been studied and applied to the monthly malaria morbidity cases from both district and regional health facilities in Ghana. The study sought to explore the …

    edithcowan Repository record for Space-time statistical analysis of malaria morbidity incidence cases in Ghana: A geostatistical modelling approach (opens in a new tab)

  10. Influenza Modeling, New York City, January 2000 to March 2010

    … was to compare Poisson cyclical regression, Seasonal Autoregressive Integrated Moving Average (SARIMA), and General Additive Modeling (GAM) statistical modeling techniques to find the optimal modeling technique for ILI visits from January 2002 to June 2007. The optimal modeling technique was …

    south-carolina Repository record for Influenza Modeling, New York City, January 2000 to March 2010 (opens in a new tab)

  11. Does the inclusion of climate variables improve tourism demand forecasting performance?

    … of the bounds test cointegration approach, the autoregressive distributed lag model (ADLM), the leading indicator (LI) model, the vector autoregressive (VAR) model, the time-varying parameter (TVP) model and the simple dynamic (SD) model, take two model specifications, which are different in …

    bournemouth Repository record for Does the inclusion of climate variables improve tourism demand forecasting performance? (opens in a new tab)