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Learn about time series ARIMA models in SPSS with data from the NOAA Global Climate at a Glance (1910-2015) / the Odum Institute.

By: Material type: TextPublisher: London : SAGE Publications, Ltd., 2017Description: 1 online resource : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781473995321 (online resource) :
Subject(s): DDC classification:
  • 519.55
Online resources: This dataset example introduces researchers to autoregressive integrated moving average (ARIMA) models for a single time series variable. An ARIMA model is a statistical model used to estimate the temporal dynamics of an individual times series. ARIMA models are frequently used for forecasting future values of the time series in question. This example uses a subset of data from the United States National Oceanic and Atmospheric Administration (NOAA) Climate at a Glance website. It examines the temporal dynamics in average annual land temperatures in Asia from 1910 to 2015. Understanding trends in global temperature will help researchers and policy makers better understand potential climate change and plan for its impact. The sample dataset used for this example has been cleaned and organized to make this example easier to follow. Interested readers should read the full documentation for the dataset before using it for research (https://www.ncdc.noaa.gov/cag/time-series/global).Direct Prerequisites: Time Series ACFs and PACFs
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This dataset example introduces researchers to autoregressive integrated moving average (ARIMA) models for a single time series variable. An ARIMA model is a statistical model used to estimate the temporal dynamics of an individual times series. ARIMA models are frequently used for forecasting future values of the time series in question. This example uses a subset of data from the United States National Oceanic and Atmospheric Administration (NOAA) Climate at a Glance website. It examines the temporal dynamics in average annual land temperatures in Asia from 1910 to 2015. Understanding trends in global temperature will help researchers and policy makers better understand potential climate change and plan for its impact. The sample dataset used for this example has been cleaned and organized to make this example easier to follow. Interested readers should read the full documentation for the dataset before using it for research (https://www.ncdc.noaa.gov/cag/time-series/global).Direct Prerequisites: Time Series ACFs and PACFs

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