The focus of this paper is to evaluate if forecast produced by the Central Bank of Sweden (Riksbanken) for the 12 month change in the consumer price index is unbiased? Results shows that for shorter horizons (h < 12) the mean forecast error is unbiased but for longer horizons its negatively biased when inference is done by Maximum entropy bootstrap technique. Can the unbiasedness be improved by strict ap- pliance to econometric methodology? Forecasting with a linear univariate model (seasonal ARIMA) and a multivariate model Vector Error Correction model (VECM) shows that when controlling for the presence of structural breaks VECM outperforms both prediction produced Riksbanken and ARIMA. However Riksbanken had the best precision in their forecast, estimated as MSFE

This study aims to investigate short-to-medium forecasting and simulation of streamflow, water levels, and sediments in Canada using Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models. The methodology can account for linear trends in the time series that may result from climate and environmental changes. A Universal Canadian forecast Application using python web interface was developed to generate short-term forecasts using SARIMA. The Akaike information criteria was used as performance criteria for generating efficient SARIMA models. The developed models were validated by analyzing the residuals. Several stations from the Canadian Hydrometric Database (HYDAT) displaying a linear upward or downward trend were identified to validate the methodology. Trends were detected using the Man-Kendall test. The Nash-Sutcliffe efficiency coefficients (Nash ad Sutcliffe, 1970) of the developed models indicate that they are acceptable. The models can be used for short term (1 to 7 days) and medium-term (7 days to six months) forecasting of streamflow, water levels and sediments at all Canadian hydrometric stations. Such a forecast can be used for water resources management and help mitigate the effects of floods and droughts. The models can also be used to generate long time-series that can be used to test the performance of water resources systems. Finally, we have automated the process of analysis, model-building and forecasting streamflow, water levels, and sediments by building a python-based application easily extendable and user-friendly. Therefore, automating the SARIMA calibration and forecasting process for all Canadian stations for the HYDAT database will prove to be a very useful tool for decision-makers and other entities in the field of hydrological study.

On a group level, satisfaction with botulinum neurotoxin (BoNT) treatment in neurological indications is high. However, it is well known that a relevant amount of patients may not respond as expected. The aim of this study is to evaluate the BoNT treatment outcome on an individual level using a statistical single-case analysis as an adjunct to traditional group statistics. The course of the daily perceived severity of symptoms across a BoNT cycle was analyzed in 20 cervical dystonia (CD) and 15 hemifacial spasm (HFS) patients. A parametric single-case autoregressive integrated moving average (ARIMA) time series analysis was used to detect individual responsiveness to BoNT treatment. Overall, both CD and HFS patients significantly responded to BoNT treatment with a gradual worsening of symptom intensities towards BoNT reinjection. However, only 8/20 CD patients (40%) and 5/15 HFS patients (33.3%) displayed the expected U-shaped curve of BoNT efficacy across a single treatment cycle. CD (but not HFS) patients who followed the expected outcome course had longer BoNT injection intervals, showed a better match to objective symptom assessments, and were characterized by a stronger certainty to control their somatic symptoms (i.e., internal medical locus of control). In addition to standard evaluation procedures, patients should be identified who do not follow the mean course-of-treatment effect. Thus, the ARIMA single-case time series analysis seems to be an appropriate addition to clinical treatment studies in order to detect individual courses of subjective symptom intensities.

Forecasting is an important analysis task and there is a need of integrating time series models and estimation methods in database systems. The main issue is the computationally expensive maintenance of model parameters when new data is inserted. In this paper, we examine how an important class of time series models, the AutoRegressive Integrated Moving Average (ARIMA) models, can be maintained with respect to inserts. Therefore, we propose a novel approach, on-demand estimation, for the efficient maintenance of maximum likelihood estimates from numerically implemented estimators. We present an extensive experimental evaluation on both real and synthetic data, which shows that our approach yields a substantial speedup while sacrificing only a limited amount of predictive accuracy.

<p>This thesis is submitted by Weiping Tang on August 2, 2011.</p> / <p>Several control schemes for monitoring process mean shifts, including cumulative sum (CUSUM), weighted cumulative sum (WCUSUM), adaptive cumulative sum (ACUSUM) and exponentially weighted moving average (EWMA) control schemes, display high performance in detecting constant process mean shifts. However, a variety of dynamic mean shifts frequently occur and few control schemes can efficiently work in these situations due to the limited window for catching shifts, particularly when the mean decreases rapidly. This is precisely the case when one uses the residuals from autocorrelated data to monitor the process mean, a feature often referred to as forecast recovery. This thesis focuses on detecting a shift in the mean of a time series when a forecast recovery dynamic pattern in the mean of the residuals is observed. Specifically, we examine in detail several particular cases of the Autoregressive Integrated Moving Average (ARIMA) time series models. We introduce a new upper-sided control chart based on the Exponentially Weighted Moving Average (EWMA) scheme combined with the Fast Initial Response (FIR) feature. To assess chart performance we use the well-established Average</p> <p>Run Length (ARL) criterion. A non-homogeneous Markov chain method is developed for ARL calculation for the proposed chart. We show numerically that the proposed procedure performs as well or better than the Weighted Cumulative Sum (WCUSUM) chart introduced by Shu, Jiang and Tsui (2008), and better than the conventional CUSUM, the ACUSUM and the Generalized Likelihood Ratio Test (GLRT) charts. The methods are illustrated on molecular weight data from a polymer manufacturing process.</p> / Master of Science (MSc)

Power electronic devices have become integral components in modern consumer and transportation industries. Predicting the failure or health status of these devices not only ensures operational safety and prevents catastrophic consequences but also leads to reduced downtime and operational costs. However, failure or health status prediction represents a complex problem marked by numerous intrinsic and extrinsic variables, leading to different lifetimes of devices. Additionally, selecting relevant precursor signals that effectively capture the underlying failure mechanisms and overcoming time-series prediction challenges, such as handling dynamic and non-linear behaviors, are crucial for accurate predictions. In the thesis, three models—Kalman filter (KF), Particle filter (PF), and Autoregressive Integrated Moving Average (ARIMA)—are applied, compared, and evaluated for failure or health status prediction of power electronic devices using Power Cycling (PC) test data for power diodes. Among the models, the KF demonstrates the most significant performance while consuming the least amount of time. The PF achieves the second-best performance and the third-best time consumption. Meanwhile, the in-sample ARIMA model delivers the third-best performance and the second-best time consumption. Finally, the out-of-sample ARIMA model ranked the lowest in both performance and time consumption. These results suggest that dynamic models, specifically the KF and PF, exhibit superior generalization capabilities across different devices. This underscores the potential of dynamic models for enhancing predictive accuracy while optimizing computational efficiency in the context of real-time power electronic device health monitoring. / Effektelektronikkomponenter har blivit integrerade delar av moderna konsument- och transportindustrier. Att förutsäga fel eller hälsotillstånd hos dessa enheter säkerställer inte bara operativ säkerhet och förebygger katastrofala konsekvenser utan leder också till minskad driftstopp och lägre driftskostnader. Dock representerar förutsägelse av fel eller hälsotillstånd en komplex uppgift som kännetecknas av många inbyggda och yttre variabler, vilket leder till olika livslängder för enheterna. Dessutom är det avgörande för noggranna förutsägelser att välja relevanta föregångssignaler som effektivt fångar upp de underliggande felmekanismerna och övervinna utmaningar med tidsberoende prediktion, såsom hantering av dynamiska och icke-linjära beteenden. I avhandlingen tillämpas, jämförs och utvärderas tre modeller - Kalman-filter (KF), partikelfilter (PF) och autoregressiv integrerad rörlig medelvärde (ARIMA) - för förutsägelse av fel eller hälsotillstånd hos effektelektronikkomponenter med hjälp av testdata för effektdioder från Power Cycling (PC). Bland modellerna visar KF den mest betydande prestandan samtidigt som den kräver minst tid. PF uppnår den näst bästa prestandan och den tredje bästa tidsåtgången. Samtidigt ger in-sample ARIMA-modellen den tredje bästa prestandan och den näst bästa tidsåtgången. Slutligen rankades out-of-sample ARIMA-modellen lägst både när det gäller prestanda och tidsåtgång. Dessa resultat tyder på att dynamiska modeller, särskilt KF och PF, uppvisar överlägsna generaliseringsförmågor över olika enheter. Detta understryker potentialen hos dynamiska modeller för att förbättra förutsägelseprecisionen samtidigt som de optimerar beräkningskapaciteten i sammanhanget av övervakning av hälsotillståndet för effektelektronikkomponenter i realtid.

The main objective of the thesis was to investigate the feasibility of accurately and precisely fore- casting the number of both national and provincial bene ciaries of social security grants in South Africa, using simple autoregressive integrated moving average (ARIMA) models. The series of the monthly number of bene ciaries of the old age, child support, foster care and disability grants from April 2004 to March 2010 were used to achieve the objectives of the thesis. The conclusions from analysing the series were that: (1) ARIMA models for forecasting are province and grant-type spe- ci c; (2) for some grants, national forecasts obtained by aggregating provincial ARIMA forecasts are more accurate and precise than those obtained by ARIMA modelling national series; and (3) for some grants, forecasts obtained by modelling the latest half of the series were more accurate and precise than those obtained from modelling the full series. / Mathematical Sciences / M.Sc. (Statistics)

The main objective of the thesis was to investigate the feasibility of accurately and precisely fore- casting the number of both national and provincial bene ciaries of social security grants in South Africa, using simple autoregressive integrated moving average (ARIMA) models. The series of the monthly number of bene ciaries of the old age, child support, foster care and disability grants from April 2004 to March 2010 were used to achieve the objectives of the thesis. The conclusions from analysing the series were that: (1) ARIMA models for forecasting are province and grant-type spe- ci c; (2) for some grants, national forecasts obtained by aggregating provincial ARIMA forecasts are more accurate and precise than those obtained by ARIMA modelling national series; and (3) for some grants, forecasts obtained by modelling the latest half of the series were more accurate and precise than those obtained from modelling the full series. / Mathematical Sciences / M.Sc. (Statistics)

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