Browsing by Subject "ARIMA"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Applied Time Series and Duluth Temperature Prediction(2017-06) Wan, XiangpengAutoregressive integrated moving average (ARIMA) has been one of the popular linear models in time series forecasting during the past three decades.The Triple Expo- nential Model also can be used to fit the time series data. This project takes Duluth temperature predictions as a case study, finding the best statistical model to predict the temperature. I collected 30 years of Duluth monthly maximum temperature data, from 1986 to 2016, and I fi t 29 years of them into di erent models including Triple Exponential Smoothing model, ARIMA model, and SARIMA model. Then I predicted the last year's temperature in those models, and I compared them to the true value of last year's temperature, which gave me the SSE value for each model so that I could find the best model.Item The Effect of Annual and Seasonal Variation in Precipitation on Temporal Water Storage Dynamics in Six Headwater Peatland Catchments: Marcell Experimental Forest, Minnesota(2023-06) Adams, DavidUsing data collected from six headwater peatland catchments at the Marcell Experimental Forest in northern Minnesota, I assessed the relationship between variability in annual precipitation and annual changes in catchment water storage. Three hypotheses are addressed; (1) annual variability in precipitation is a primary driver of catchment storage change, (2) years of below average precipitation drive the relationship between precipitation and catchment water storage change, and (3) winter and fall precipitation variability are significant seasonal drivers of the annual catchment water storage change. The above relationships were analyzed via cross-correlation lag analysis and linear regression analysis of long-term precipitation, peatland water table elevation (WTE), and upland soil moisture (SM) time series, where WTE and SM served to quantify catchment water storage. Results indicate strong correlations between annual water storage change and annual precipitation variability, both in contemporaneous and antecedent years. Concurrent fall precipitation and antecedent winter precipitation were found to have the most influence on a given year’s water storage change. Years in which precipitation fell below the catchment average (dry years) exhibited a moderately significant linear relationship with annual catchment water storage change. Results of the above analysis were used to create a series of multivariate linear regression models, both with and without moving-average (MA) errors; these models were able to explain between approximately 50% and 70% of the variance found in the annual water storage change time series. Boreal peatlands play a vital role in the planet’s carbon cycle; developing a better understanding of the hydrologic function of these environments will likely prove important to future climate management practices.