by Lawrence Chi-Ho Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-97). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Forecasting --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter Chapter 2 --- Holt-Winters Forecasting Procedure --- p.6 / Chapter 2.1 --- Exponential Smoothing and Holt-Winters Method --- p.6 / Chapter 2.2 --- Relationships Between Holt-Winters models and ARIMA Models --- p.13 / Chapter 2.2.1 --- A Steady Model --- p.14 / Chapter 2.2.2 --- A Growth Model --- p.15 / Chapter 2.2.3 --- The Three-Parameter Holt-Winters Model --- p.18 / Chapter 2.3 --- Some Practical Issues --- p.19 / Chapter 2.3.1 --- Normalizing the Seasonal Factors --- p.20 / Chapter 2.3.2 --- Choosing Starting Values --- p.20 / Chapter 2.3.3 --- Choosing the Smoothing Parameters --- p.22 / Chapter Chapter 3 --- Methods of Constructing Simultaneous Prediction Intervals --- p.24 / Chapter 3.1 --- Three Approximation Procedures --- p.25 / Chapter 3.1.1 --- Bonferroni-type Inequality --- p.26 / Chapter 3.1.2 --- Product-type Inequality --- p.28 / Chapter 3.1.3 --- Chi-square-type Inequality --- p.30 / Chapter 3.2 --- The 'Exact' Procedure --- p.31 / Chapter 3.3 --- Summary --- p.32 / Chapter Chapter 4 --- An Illustrative Example --- p.33 / Table 4.1 - 4.7 --- p.47 / Figure 4.1 - 4.5 --- p.55 / Chapter Chapter 5 --- Simulation Study --- p.60 / Chapter 5.1 --- Holt-Winters Forecasting Procedure for Optimal Model --- p.60 / Chapter 5.2 --- Holt-Winters Forecasting Procedure for Some Non-optimal Models --- p.66 / Chapter 5.3 --- A Comparison of Box-Jenkins Method and Holt-Winters Forecasting Procedure --- p.68 / Chapter 5.4 --- Conclusion --- p.74 / Table 5.1-5.10 --- p.75 / Chapter Chapter 6 --- Further Research --- p.82 / APPENDIXES --- p.87 / REFERENCES --- p.91

Durability tests are important to ensure the safety and reliability of a ground vehicle and involve frequently driving a vehicle through a series of events that simulate different road conditions or obstacles encountered during actual driving. Since durability tests are costly in-terms of time and money, accelerated durability lab tests can be used to spot failures before actual road tests. Signals of different events of the actual durability road tests generate three continuous time series data, that can be used to conduct accelerated durability lab tests. The actual analysis of these time series is very challenging because they are (i) of high frequency (ii) very noisy and (iii) inconsistent. The purpose of this study was to identify the patterns of signals from the noisy and inconsistent time series data collected from the field tests. The Box-Jenkins methodology was used to identify models corresponding to different events. Due to complex structures of the real data, ARMA modelling was considered after testing stationarity of the given time series. While the time series data in vertical direction was used to identify the first three events, the time series in vertical, longitudinal and lateral directions were used to identify other four events.

Durability tests are important to ensure the safety and reliability of a ground vehicle and involve frequently driving a vehicle through a series of events that simulate different road conditions or obstacles encountered during actual driving. Since durability tests are costly in-terms of time and money, accelerated durability lab tests can be used to spot failures before actual road tests. Signals of different events of the actual durability road tests generate three continuous time series data, that can be used to conduct accelerated durability lab tests. The actual analysis of these time series is very challenging because they are (i) of high frequency (ii) very noisy and (iii) inconsistent. The purpose of this study was to identify the patterns of signals from the noisy and inconsistent time series data collected from the field tests. The Box-Jenkins methodology was used to identify models corresponding to different events. Due to complex structures of the real data, ARMA modelling was considered after testing stationarity of the given time series. While the time series data in vertical direction was used to identify the first three events, the time series in vertical, longitudinal and lateral directions were used to identify other four events.

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains x, 111 p. : ill. Includes abstract. Includes bibliographical references (p. 102-106).

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This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some of the modifications made to the original method to make it suitable for use with a broad range of time series. Forecasts were combined by means of both the simple average and a weighted averaging scheme. Empirical and generated data were employed to perform the forecasting evaluation. The 111 sets of empirical data came from the M-Competition. The twenty-one sets of generated data arose from ARIMA models that Box, Taio and Pack analyzed using the Box-Jenkins method. To compare the forecasting abilities of the Box-Jenkins and the automated ARIMA alone and in combination with the other two methods, two accuracy measures were used. These measures, which are free of magnitude bias, are the mean absolute percentage error (MAPE) and the median absolute percentage error (Md APE).

Enligt det antagna klimatmålet år 2017 ska Sverige vara utsläppsneutralt år 2045. Detta innebär att Sveriges utsläpp av växthusgaser måste minska kraftigt. I denna studie studeras utsläpp från olika sektorer och genom en tidsserieanalys prediceras framtida utsläppsvärden fram. Resultaten i studien visar att utsläppen förväntas minska inom samtliga studerade sektorer men att ytterligare forskning krävs för att veta om denna minskning är tillräcklig. Vidare är resultaten känsliga för förändringar, varför uppdaterade och kontinuerliga analyser rekommenderas.

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