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Showing 1 to 20 of 39 for “"Autoregressive integrated moving average"”.
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System Simulation Output Analysis by Autoregressive-Integrated Moving Average Models
Made available in DSpace on 2015-05-12T22:38:11Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 7309930.PDF: 5142666 bytes, checksum: 5ae56d3bb78e2c7de46581dcc2c2857c (MD5) Previous issue date: 1972
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Time series analysis of RTC Great Lakes recruit graduate data
… to produce a model. As an alternative the autoregressive integrated moving average (ARIMA) process is used to describe the data. In both instances, satisfactory forecasting results are attained.
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Time Series Analysis of Stock Prices Using the Box-Jenkins Approach
… of the data and forecast future values. The Autoregressive Integrated Moving Average (ARIMA) models, or Box-Jenkins methodology, are a class of linear models that are capable of representing stationary as well as nonstationary time series. ARIMA models rely heavily on autocorrelation …
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Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates
… Memory (LSTM) model to the performance of AutoRegressive Integrated Moving Average (ARIMA) and Support vector regression (SVR) when predicting forex rates of US Dollar (USD) pair with South African Rand (ZAR) using daily timeframe data obtained from the Metatrader trading platform. The LSTM …
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Backcasting and Forecasting Boreal Wetland Water Balances Using Weather Data in Reclaimed Landscapes
… backcast and forecast meteorological trends. An autoregressive integrated moving average forecasting model was used to calculate trends in historical data and predict meteorological conditions until 2030 and their effects on wetland water volumes. Rates of annual evapotranspiration exceeded …
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Monitoring the Process Mean of Autocorrelated Data
… family of models for time series data are the autoregressive integrated moving average (ARIMA) models. These models are well suited to model production processes, in which the observations are autocorrelated. It is our interest to examine these models. Meaning is given to the process being …
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Time Series Analysis of Fine Particulates Matter (PM2.5) in Chaoyang District of Beijing
… 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 94.5μg/m³ …
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Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting
… statistical methods, including regression, autoregressive integrated moving average (ARIMA), and generalised additive models, were compared alongside machine learning methods including extreme gradient boosting, support vector machines, and long short-term memory networks. Extreme gradient …
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Predicting social unrest events in South Africa using LSTM neural networks
… traditional forecast method selected being the Autoregressive Integrated Moving Average (ARIMA model). The type of neural network implemented was the Long Short-Term Memory (LSTM) neural network. The basic theoretical concepts of ARIMA and LSTM neural networks are explained and subsequently, the …
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Anomaly detection in semiconductor manufacturing through time series forecasting using neural networks
… for comparison. The three models are namely: Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP) and Long Short Term Memory (LSTM). ARIMA is a statistical model while MLP and LSTM are neural network models. The results from the control experiment, under supervised …
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Neuro-Fuzzy Forecasting of Tourist Arrivals
… model, the basic structural model, the autoregressive integrated moving average model and the naive model. Japan was chosen as the country of study mainly due to the availability of reliable tourism data, and also because it is a popular travel destination for both business and pleasure. …
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Demand forecasting and decision making under uncertainty for long-term production planning in aviation industry
… A comparison of the modified Brownian motion and Autoregressive Integrated Moving Average model is discussed.</p> <p>The second study compared several popular decision-making methods: Expected Utility, Robust Decision Making and Information Gap. The comparison is conducted in the situation of deep …
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Forecasting short term trucking rates
… is a neural network based on Nonlinear Autoregressive Models with eXogenous input (NARX) models. NARX models are powerful when modelling complex, nonlinear and dynamic systems, especially time series. Traditional time series models, including autoregressive integrated moving average …
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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 …
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SARIMA Short to Medium-Term Forecasting and Stochastic Simulation of Streamflow, Water Levels and Sediments Time Series from the HYDAT Database
… 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 python web …
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From Seasonality to Causality: Understanding Urban Water Usage Using Statistical and Machine Learning Models
… time series modeling, including a Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) model to identify seasonal trends and assess predictive power in water usage. Results indicate a steady decline in overall water usage since 2020. Geographic …
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Past and future responses of soil water to climate change in tropical and subtropical rainforest systems in South America
… continent through maps and simulation with the Autoregressive Integrated Moving Average Model (ARIMA) making the forecast of the future climatic scenario based on the El Niño- Southern Oscillation (ENOS), meteorological systems. The use of Shared Socioeconomic Pathways (SSP) integrates the …
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The Development of Measurement and Characterization Techniques of Road Profiles
… non-stationary road profile data using ARIMA (Autoregressive Integrated Moving Average) modeling techniques. The first step is to consider the road to be a realization of an underlying stochastic process. The model identification techniques are demonstrated. Statistical techniques are developed …
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Strategic customer relationship marketing and re-intermediation models in the insurance industry
… its future growth and profitability through an integrated business model? How has the performance of existing distribution channels been affected by the advent of price comparison models? A wide range of statistical models and data mining tools were applied to this research, including vector …
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Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods
… different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and …
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