Modeling and forecasting of metrological factors using arch process under different errors distribution specification
(2021) In Mausam 72(2). p.301-312- Abstract
Various weather phenomenon are difficult to model and forecast with high precision. This study has modelled and forecasted the various parameter namely maximum and minimum temperature, morning and evening relative humidity using parametric models namely Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive conditional heteroskedasticity (GARCH)) models. The data consisted of daily time series data for Hoshangabad district of Madhya Pradesh from January, 1996 to November, 2019. The AIC and BIC criterion were used to select among competing models. Present investigation has revealed that ARIMA-GARCH models are more suitable for forecasting of minimum temperature, maximum temperature and relative humidity.
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https://lup.lub.lu.se/record/6ee181fe-fca5-4584-ad31-854f062d4a90
- author
- Mishra, Pradeep ; Fatih, Chellai ; Vani, G. K. ; Lavrod, Jakob Mattias ; Mishra, P. C. ; Choudhary, Arun Kumar ; Jain, Vikas and Dubey, Anurag
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ARCH, Error distribution, GARCH, Time series, Weather
- in
- Mausam
- volume
- 72
- issue
- 2
- pages
- 12 pages
- publisher
- India Meteorological Department
- external identifiers
-
- scopus:85107947634
- ISSN
- 0252-9416
- language
- English
- LU publication?
- no
- id
- 6ee181fe-fca5-4584-ad31-854f062d4a90
- date added to LUP
- 2021-07-14 14:36:45
- date last changed
- 2022-04-27 02:47:33
@article{6ee181fe-fca5-4584-ad31-854f062d4a90, abstract = {{<p>Various weather phenomenon are difficult to model and forecast with high precision. This study has modelled and forecasted the various parameter namely maximum and minimum temperature, morning and evening relative humidity using parametric models namely Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive conditional heteroskedasticity (GARCH)) models. The data consisted of daily time series data for Hoshangabad district of Madhya Pradesh from January, 1996 to November, 2019. The AIC and BIC criterion were used to select among competing models. Present investigation has revealed that ARIMA-GARCH models are more suitable for forecasting of minimum temperature, maximum temperature and relative humidity.</p>}}, author = {{Mishra, Pradeep and Fatih, Chellai and Vani, G. K. and Lavrod, Jakob Mattias and Mishra, P. C. and Choudhary, Arun Kumar and Jain, Vikas and Dubey, Anurag}}, issn = {{0252-9416}}, keywords = {{ARCH; Error distribution; GARCH; Time series; Weather}}, language = {{eng}}, number = {{2}}, pages = {{301--312}}, publisher = {{India Meteorological Department}}, series = {{Mausam}}, title = {{Modeling and forecasting of metrological factors using arch process under different errors distribution specification}}, volume = {{72}}, year = {{2021}}, }