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Forecasting With time Series Models
2002Having selected a model and fitted its parameters to a given times series, the model can then be used to estimate new data of the time series. If such data are estimated for a time period following the final data value X T of the given time series, we speak of a prediction or forecast.
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2012
Nowcasting global solar irradiance on very short time horizons is the principal topic discussed in this chapter. Various ARIMA models for nowcasting clearness index are inferred and assessed. Radiometric data measured at 15 s lag during June 2010 in Timisoara (Romania) are used for setting up and testing the models.
Marius Paulescu +3 more
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Nowcasting global solar irradiance on very short time horizons is the principal topic discussed in this chapter. Various ARIMA models for nowcasting clearness index are inferred and assessed. Radiometric data measured at 15 s lag during June 2010 in Timisoara (Romania) are used for setting up and testing the models.
Marius Paulescu +3 more
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2008
La previsione della domanda commerciale viene generata mediante l’impiego di modelli matematici di Sales Forecasting, i quali analizzano i valori disponibili delle vendite realizzate nel passato, interpretano i fenomeni di regolarita nella domanda e proiettano le componenti delle serie storiche nel futuro, dando luogo al piano previsionale di domanda ...
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La previsione della domanda commerciale viene generata mediante l’impiego di modelli matematici di Sales Forecasting, i quali analizzano i valori disponibili delle vendite realizzate nel passato, interpretano i fenomeni di regolarita nella domanda e proiettano le componenti delle serie storiche nel futuro, dando luogo al piano previsionale di domanda ...
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2011
This chapter includes two problems for forecasting of a time series using past data points. It is argued that the past data points used for forecasting of the future data points should be strongly correlated with each other. It is illustrated that the strongly correlated past data points can be identified from the autocorrelation function of the time ...
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This chapter includes two problems for forecasting of a time series using past data points. It is argued that the past data points used for forecasting of the future data points should be strongly correlated with each other. It is illustrated that the strongly correlated past data points can be identified from the autocorrelation function of the time ...
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2019
Forecasting is important in economics, commerce and various disciplines of social science and pure science. Forecasting is a method for computing future values by analysing the behaviour of present and past values of a time series. Forecasting model may be univariate or multivariate.
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Forecasting is important in economics, commerce and various disciplines of social science and pure science. Forecasting is a method for computing future values by analysing the behaviour of present and past values of a time series. Forecasting model may be univariate or multivariate.
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Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
ACM Computing Surveys, 2023Konstantinos Benidis +2 more
exaly
Introduction to Time Series and Forecasting.
Journal of the American Statistical Association, 1997Ramalingam Shanmugam +2 more
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An Experimental Review on Deep Learning Architectures for Time Series Forecasting
International Journal of Neural Systems, 2021Pedro Lara-Benítez +2 more
exaly