Natural gas storage level forecasting using temperature data

Even though the theory of storage is historically a popular view to explain commodity futures prices, many authors focus on the oil price link. Past studies have shown an increased futures price volatility on Mondays and days when natural gas storage levels are released, which could both implicate t...

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Bibliographic Details
Main Author:	Sundin, Daniel
Format:	Others
Language:	English
Published:	Linköpings universitet, Produktionsekonomi 2020
Subjects:	Natural gas storage forecasting Natural gas storage Natural gas Storage forecasting Forecasting Futures Futures forecasting Natural gas futures forecasting Least squares regression Regression Machine learning Exponential Weighted Moving Average Moving Average EWMA MA Seasonality Commodity futures Optimisation Inverse problems Consumption forecasting Production Forecasting Residential Commercial Industrial Electric power NOAA EIA Pipelines Polynomial Weather stations Mathematics Matematik
Online Access:	http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169856

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spelling	ndltd-UPSALLA1-oai-DiVA.org-liu-1698562020-12-15T05:29:11ZNatural gas storage level forecasting using temperature dataengSundin, DanielLinköpings universitet, Produktionsekonomi2020Natural gas storage forecastingNatural gas storageNatural gasStorage forecastingForecastingFuturesFutures forecastingNatural gas futures forecastingLeast squares regressionRegressionMachine learningExponential Weighted Moving AverageMoving AverageEWMAMASeasonalityCommodity futuresOptimisationInverse problemsConsumption forecastingProduction ForecastingResidentialCommercialIndustrialElectric powerNOAAEIAPipelinesPolynomialWeather stationsMathematicsMatematikEven though the theory of storage is historically a popular view to explain commodity futures prices, many authors focus on the oil price link. Past studies have shown an increased futures price volatility on Mondays and days when natural gas storage levels are released, which could both implicate that storage levels and temperature data are incorporated in the prices. In this thesis, the U.S. natural gas storage level change is studied as a function of the consumption and production. Consumption and production are furthered segmented and separately forecasted by modelling inverse problems that are solved by least squares regression using temperature data and timeseries analysis. The results indicate that each consumer consumption segment is highly dependent of the temperature with R2-values of above 90%. However, modelling each segment completely by time-series analysis proved to be more efficient due to lack of flexibility in the polynomials, lack of used weather stations and seasonal patterns in addition to the temperatures. Although the forecasting models could not beat analysts’ consensus estimates, these present natural gas storage level drivers and can thus be used to incorporate temperature forecasts when estimating futures prices. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169856application/pdfinfo:eu-repo/semantics/openAccess
collection	NDLTD
language	English
format	Others
sources	NDLTD
topic	Natural gas storage forecasting Natural gas storage Natural gas Storage forecasting Forecasting Futures Futures forecasting Natural gas futures forecasting Least squares regression Regression Machine learning Exponential Weighted Moving Average Moving Average EWMA MA Seasonality Commodity futures Optimisation Inverse problems Consumption forecasting Production Forecasting Residential Commercial Industrial Electric power NOAA EIA Pipelines Polynomial Weather stations Mathematics Matematik
spellingShingle	Natural gas storage forecasting Natural gas storage Natural gas Storage forecasting Forecasting Futures Futures forecasting Natural gas futures forecasting Least squares regression Regression Machine learning Exponential Weighted Moving Average Moving Average EWMA MA Seasonality Commodity futures Optimisation Inverse problems Consumption forecasting Production Forecasting Residential Commercial Industrial Electric power NOAA EIA Pipelines Polynomial Weather stations Mathematics Matematik Sundin, Daniel Natural gas storage level forecasting using temperature data
description	Even though the theory of storage is historically a popular view to explain commodity futures prices, many authors focus on the oil price link. Past studies have shown an increased futures price volatility on Mondays and days when natural gas storage levels are released, which could both implicate that storage levels and temperature data are incorporated in the prices. In this thesis, the U.S. natural gas storage level change is studied as a function of the consumption and production. Consumption and production are furthered segmented and separately forecasted by modelling inverse problems that are solved by least squares regression using temperature data and timeseries analysis. The results indicate that each consumer consumption segment is highly dependent of the temperature with R2-values of above 90%. However, modelling each segment completely by time-series analysis proved to be more efficient due to lack of flexibility in the polynomials, lack of used weather stations and seasonal patterns in addition to the temperatures. Although the forecasting models could not beat analysts’ consensus estimates, these present natural gas storage level drivers and can thus be used to incorporate temperature forecasts when estimating futures prices.
author	Sundin, Daniel
author_facet	Sundin, Daniel
author_sort	Sundin, Daniel
title	Natural gas storage level forecasting using temperature data
title_short	Natural gas storage level forecasting using temperature data
title_full	Natural gas storage level forecasting using temperature data
title_fullStr	Natural gas storage level forecasting using temperature data
title_full_unstemmed	Natural gas storage level forecasting using temperature data
title_sort	natural gas storage level forecasting using temperature data
publisher	Linköpings universitet, Produktionsekonomi
publishDate	2020
url	http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169856
work_keys_str_mv	AT sundindaniel naturalgasstoragelevelforecastingusingtemperaturedata
_version_	1719370478587478016

Natural gas storage level forecasting using temperature data

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