Korttidsprognoser av Kalixälvens vattenflöde
(2023) STAH11 20212Department of Statistics
- Abstract
- The Kalix River, located in the northern part of Sweden, experiences a slowdown
in water flow during cold winters. However, in spring, when snow and ice melt in
the mountains, the water flow starts to increase. Towards the end of spring, there
is usually a rapid and unpredictable surge in water flow known as the spring flood.
The arrival of the spring flood is stochastic in time and does not occur on the same
day or even the same week each year. Therefore, accurate forecasts for the arrival of
the spring flood are essential and this thesis aims to improve short-term predictions
of water flow.
The seasonal dependence and variability of the water flow brings to mind whether
some recursive and dynamic models would make better... (More) - The Kalix River, located in the northern part of Sweden, experiences a slowdown
in water flow during cold winters. However, in spring, when snow and ice melt in
the mountains, the water flow starts to increase. Towards the end of spring, there
is usually a rapid and unpredictable surge in water flow known as the spring flood.
The arrival of the spring flood is stochastic in time and does not occur on the same
day or even the same week each year. Therefore, accurate forecasts for the arrival of
the spring flood are essential and this thesis aims to improve short-term predictions
of water flow.
The seasonal dependence and variability of the water flow brings to mind whether
some recursive and dynamic models would make better predictions for this river
than a simple autoregressive (AR) model. The predictive ability of the AR model of
order five is compared to three recursive models, relying on this AR(5) model. These
models use the estimation methods; forgetting factor, sliding window and Kalman
filter. The coefficients in the recursive models are dynamically estimated and time
adjusted.
The performance of each model is evaluated, and it is observed that all models
perform well for one-day-ahead predictions, but show slightly lower accuracy for
three-day-ahead predictions. Despite the initial expectation of improved predictions
with dynamic models, there was no significant improvement compared with the more
simple AR(5) model in any case. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9113882
- author
- Carlsson, Mathilda LU
- supervisor
- organization
- course
- STAH11 20212
- year
- 2023
- type
- M2 - Bachelor Degree
- subject
- keywords
- ARIMA, RecursiveAR, forgetting factor, sliding window, Kalman filter, short-term predictions, water flows
- language
- Swedish
- id
- 9113882
- date added to LUP
- 2023-05-04 08:50:09
- date last changed
- 2023-05-04 08:50:09
@misc{9113882, abstract = {{The Kalix River, located in the northern part of Sweden, experiences a slowdown in water flow during cold winters. However, in spring, when snow and ice melt in the mountains, the water flow starts to increase. Towards the end of spring, there is usually a rapid and unpredictable surge in water flow known as the spring flood. The arrival of the spring flood is stochastic in time and does not occur on the same day or even the same week each year. Therefore, accurate forecasts for the arrival of the spring flood are essential and this thesis aims to improve short-term predictions of water flow. The seasonal dependence and variability of the water flow brings to mind whether some recursive and dynamic models would make better predictions for this river than a simple autoregressive (AR) model. The predictive ability of the AR model of order five is compared to three recursive models, relying on this AR(5) model. These models use the estimation methods; forgetting factor, sliding window and Kalman filter. The coefficients in the recursive models are dynamically estimated and time adjusted. The performance of each model is evaluated, and it is observed that all models perform well for one-day-ahead predictions, but show slightly lower accuracy for three-day-ahead predictions. Despite the initial expectation of improved predictions with dynamic models, there was no significant improvement compared with the more simple AR(5) model in any case.}}, author = {{Carlsson, Mathilda}}, language = {{swe}}, note = {{Student Paper}}, title = {{Korttidsprognoser av Kalixälvens vattenflöde}}, year = {{2023}}, }