Forecasting daily maximum temperature of Umeå
The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily...
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Umeå universitet, Institutionen för matematik och matematisk statistik
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ndltd-UPSALLA1-oai-DiVA.org-umu-1124042016-01-26T05:06:40ZForecasting daily maximum temperature of Umeåengnaz, saimaUmeå universitet, Institutionen för matematik och matematisk statistik2015ARIMA modelsexponential smoothingcubic splinestate-space modelvector autoregression.ARIMA modellerexponential smoothingkubiska splinesstate-space modellvektor autoregression.The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily maximum temperatures of Umeå, and compare the performance of some methods in forecasting these temperatures. Here we analyse the data of daily maximum temperatures and find the predictions for some local period using methods of autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and cubic splines. The forecast package in R is used for this purpose and automatic forecasting methods available in the package are applied for modelling with ARIMA, ETS, and cubic splines. The thesis begins with some initial modelling on univariate time series of daily maximum temperatures. The data of daily maximum temperatures of Umeå from 2008 to 2013 are used to compare the methods using various lengths of training period. On the basis of accuracy measures we try to choose the best method. Keeping in mind the fact that there are various factors which can cause the variability in daily temperature, we try to improve the forecasts in the next part of thesis by using multivariate time series forecasting method on the time series of maximum temperatures together with some other variables. Vector auto regressive (VAR) model from the vars package in R is used to analyse the multivariate time series. Results: ARIMA is selected as the best method in comparison with ETS and cubic smoothing splines to forecast one-step-ahead daily maximum temperature of Umeå, with the training period of one year. It is observed that ARIMA also provides better forecasts of daily temperatures for the next two or three days. On the basis of this study, VAR (for multivariate time series) does not help to improve the forecasts significantly. The proposed ARIMA with one year training period is compatible with the forecasts of daily maximum temperature of Umeå obtained from Swedish Meteorological and Hydrological Institute (SMHI). Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-112404application/pdfinfo:eu-repo/semantics/openAccess |
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ARIMA models exponential smoothing cubic spline state-space model vector autoregression. ARIMA modeller exponential smoothing kubiska splines state-space modell vektor autoregression. |
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ARIMA models exponential smoothing cubic spline state-space model vector autoregression. ARIMA modeller exponential smoothing kubiska splines state-space modell vektor autoregression. naz, saima Forecasting daily maximum temperature of Umeå |
description |
The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily maximum temperatures of Umeå, and compare the performance of some methods in forecasting these temperatures. Here we analyse the data of daily maximum temperatures and find the predictions for some local period using methods of autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and cubic splines. The forecast package in R is used for this purpose and automatic forecasting methods available in the package are applied for modelling with ARIMA, ETS, and cubic splines. The thesis begins with some initial modelling on univariate time series of daily maximum temperatures. The data of daily maximum temperatures of Umeå from 2008 to 2013 are used to compare the methods using various lengths of training period. On the basis of accuracy measures we try to choose the best method. Keeping in mind the fact that there are various factors which can cause the variability in daily temperature, we try to improve the forecasts in the next part of thesis by using multivariate time series forecasting method on the time series of maximum temperatures together with some other variables. Vector auto regressive (VAR) model from the vars package in R is used to analyse the multivariate time series. Results: ARIMA is selected as the best method in comparison with ETS and cubic smoothing splines to forecast one-step-ahead daily maximum temperature of Umeå, with the training period of one year. It is observed that ARIMA also provides better forecasts of daily temperatures for the next two or three days. On the basis of this study, VAR (for multivariate time series) does not help to improve the forecasts significantly. The proposed ARIMA with one year training period is compatible with the forecasts of daily maximum temperature of Umeå obtained from Swedish Meteorological and Hydrological Institute (SMHI). |
author |
naz, saima |
author_facet |
naz, saima |
author_sort |
naz, saima |
title |
Forecasting daily maximum temperature of Umeå |
title_short |
Forecasting daily maximum temperature of Umeå |
title_full |
Forecasting daily maximum temperature of Umeå |
title_fullStr |
Forecasting daily maximum temperature of Umeå |
title_full_unstemmed |
Forecasting daily maximum temperature of Umeå |
title_sort |
forecasting daily maximum temperature of umeå |
publisher |
Umeå universitet, Institutionen för matematik och matematisk statistik |
publishDate |
2015 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-112404 |
work_keys_str_mv |
AT nazsaima forecastingdailymaximumtemperatureofumea |
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