Global ETD Search

21	Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting Comparison Bajracharya, Dinesh January 2011 (has links) Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales. / Program: Magisterutbildning i informatik econometrics forecasting ARIMA exponential smoothing regression neural network ensemble WMAPE MAPE MAE RMSE linear correlation Engineering and Technology Teknik och teknologier
22	A Model-Based Approach to Engineer Self-Adaptive Systems with Guarantees / En modelbaserad metod för att utveckla självadaptiva system med garantier Iftikhar, Muhammad Usman January 2017 (has links) Modern software systems are increasingly characterized by uncertainties in the operating context and user requirements. These uncertainties are difficult to predict at design time. Achieving the quality goals of such systems depends on the ability of the software to deal with these uncertainties at runtime. A self-adaptive system employs a feedback loop to continuously monitor and adapt itself to achieve particular quality goals (i.e., adaptation goals) regardless of uncertainties. Current research applies formal techniques to provide guarantees for adaptation goals, typically using exhaustive verification techniques. Although these techniques offer strong guarantees for the goals, they suffer from well-known state explosion problem. In this thesis, we take a broader perspective and focus on two types of guarantees: (1) functional correctness of the feedback loop, and (2) guaranteeing the adaptation goals in an efficient manner. To that end, we present ActivFORMS (Active FORmal Models for Self-adaptation), a formally founded model-driven approach for engineering self-adaptive systems with guarantees. ActivFORMS achieves functional correctness by direct execution of formally verified models of the feedback loop using a reusable virtual machine. To efficiently provide guarantees for the adaptation goals with a required level of confidence, ActivFORMS applies statistical model checking at runtime. ActivFORMS supports on the fly changes of adaptation goals and updates of the verified feedback loop models that meet the changed goals. To demonstrate the applicability and effectiveness of the approach, we applied ActivFORMS in several domains: warehouse transportation, oceanic surveillance, tele assistance, and IoT building security monitoring. / Marie Curie CIG, FP7-PEOPLE-2011-CIG, Project ID: 303791 Self-adaptive software systems MAPE-K feedback loop Statistical model checking Analytical methods Computer Science Datavetenskap (datalogi)
23	Forecasting Monthly Swedish Air Traveler Volumes Becker, Mark, Jarvis, Peter January 2023 (has links) In this paper we conduct an out-of-sample forecasting exercise for monthly Swedish air traveler volumes. The models considered are multiplicative seasonal ARIMA, Neural network autoregression, Exponential smoothing, the Prophet model and a Random Walk as a benchmark model. We divide the out-of-sample data into three different evaluation periods: Pre-COVID-19, during COVID-19 and Post-COVID-19 for which we calculate the MAE, MAPE and RMSE for each model in each of these evaluation periods. The results show that for the Pre-COVID-19 period all models produce accurate forecasts, in comparison to the Random Walk model. For the period during COVID-19, no model outperforms the Random Walk, with only Exponential smoothing performing as well as the Random Walk. For the period Post-COVID-19, the best performing models are Random Walk, SARIMA and Exponential smoothing, with all aforementioned models having similar performance. Forecasting SARIMA Neural network autoregression Exponential smoothing the Prophet model Random Walk MAE MAPE RMSE Probability Theory and Statistics Sannolikhetsteori och statistik
24	Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors Oleksandra, Shovkun January 2014 (has links) Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) method was also used as an alternativeto OLS. The Modeling of Prediction Errors (MPE) was constructed in orderto predict errors for the existing predictions by using the Autoregression(AR) and the Autoregressive-Moving-Average (ARMA) models. For thefirst approach it is observed that the last reported value is of mainimportance. An attempt was made to improve the performance and to getbetter parameter estimates. The separation of concerns and the combinationof concerns were suggested in order to extend the constructed approachesand raise the efficacy of them. Both methods were tested on data for thefourth region of Sweden (“elområde 4”) provided by Bixia. The obtainedresults indicate that all suggested approaches reduce the total percentageerrors of prediction consumption approximately by one half. Resultsindicate that use of the ARMA model slightly better reduces the total errorsthan the other suggested approaches. The most effective way to reduce thetotal consumption prediction errors seems to be obtained by reducing thetotal errors for each subregion. Modeling of Prediction Errors ARMA OLS WLS TECP TECC TPE MAPE MPE
25	Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work Buttar, Sarpreet Singh January 2018 (has links) Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. However, verifying the entire adaptation space is often not feasible since it requires time and resources. In this thesis, we present an approach which uses machine learning to reduce the adaptation space in self-adaptive systems. The approach integrates with the feedback loop and selects a subset of the adaptation options that are valid in the current situation. The approach is applied on the simulator of a self-adaptive Internet of Things application which is deployed in KU Leuven, Belgium. We compare our results with a formal model based self-adaptation approach called ActivFORMS. The results show that on average the adaptation space is reduced by 81.2% and the adaptation time by 85% compared to ActivFORMS while achieving the same quality guarantees. Self-adaptive systems Architecture-based self-adaptation Adaptation space MAPE-K feedback loop DeltaIoT ActivFORMS Machine learning Online supervised learning Classification Regression Engineering and Technology Teknik och teknologier
26	Household’s energy consumption and productionforecasting: A Multi-step ahead forecast strategiescomparison. Martín-Roldán Villanueva, Gonzalo January 2017 (has links) In a changing global energy market where the decarbonization of the economy and the demand growth are pushing to look for new models away from the existing centralized non-renewable based grid. To do so, households have to take a ‘prosumer’ role; to help them take optimal actions is needed a multi-step ahead forecast of their expected energy production and consumption. In multi-step ahead forecasting there are different strategies to perform the forecast. The single-output: Recursive, Direct, DirRec, and the multi-output: MIMO and DIRMO. This thesis performs a comparison between the performance of the differents strategies in a ‘prosumer’ household; using Artificial Neural Networks, Random Forest and K-Nearest Neighbours Regression to forecast both solar energy production and grid input. The results of this thesis indicates that the methodology proposed performs better than state of the art models in a more detailed household energy consumption dataset. They also indicate that the strategy and model of choice is problem dependent and a strategy selection step should be added to the forecasting methodology. Additionally, the performance of the Recursive strategy is always far from the best while the DIRMO strategy performs similarly. This makes the latter a suitable option for exploratory analysis. Multi-step forecast strategies Recursive Direct DirRec DIRMO MIMO Artificial Neural Networks Random Forest K-Nearest Neighbours Regression MAPE MAE Social Sciences Interdisciplinary
27	出國觀光旅客需求預測模式建立之研究李旭煌, Lee, Shiung Hwang Unknown Date (has links) 自民國69年政府開放國人出國觀光之後，由於國民所得的提高、台幣的升值及其它種種社經有利因素的影響，使得每年出國觀光人數穩定的成長，而在民國76年開放國人赴大陸探親之後，出國觀光人數更呈直線上升，這對於提高國家知名度以及展示國家整體經濟實力有極為明顯的助益。出國觀光旅客人數的多寡直接或間接影響本地觀光業者及政府相關單位對觀光業軟、硬體設施的投資以及整體策略的規劃，舉凡國際航線的開拓、航空公司航線的增減、導遊人員的培訓以及政府駐外單位的配合措施，在在都有賴於對未來需求的精確預測，過於粗略或不當的預測，不僅將造成大量觀光資源的閒置與浪費，也將使得政府與觀光業者在這場日趨激烈的觀光事業競爭中處於極不利的地位。本研究搜集並參考近十年來國內外學者在觀光旅遊預測模式方面的研究，針對出國觀光旅客整體及各主要市場需求，尋找並建立適當之長短期預測模式。我們考慮下列六種模式：簡算法、單變量時間序列模式、轉移函數模式、時間趨勢模式、指數平滑法以及計量經濟模式，同時利用各類模式選取準則如AIC、SBC等來選取最佳模式，或以平均絕對百分誤差(MA PE)、根均方百分誤差(RMSPE)、方向變化誤差(Direction of Change Erro r)以及趨勢變化誤差(Trend Change Error)來評估各模式預測能力，從中選出最佳模式並進行預測整合分析。
28	台灣地區失業率之預測分析 / Preditive Analysis of Unemployment Rate in Taiwan 陳依鋒, Chen, Yi-Feng Unknown Date (has links) 近年來由於亞洲金融風暴的肆虐，產生經濟不景氣，使得失業的問題逐漸受到社會所關注，本論文企圖以三個時間序列方法：1.單變量ARIMA模型；2.轉換函數(TF)模型；3.向量自迴歸(VAR)模型來建立台灣地區的失業率時間序列預測模型。資料則是利用台灣地區民國75年1月至民國87年12月的失業率月資料作實證預測分析，為了知道資料是否來自時間趨勢模型，測試是否經過差分消掉一部份的記憶會發生預測的誤差，所以先以多步(multi-step)預測和一步(one-step)預測的方法計算出民國88年1月至88年12月預測值，而預測評估準則則採用(1)MAPE、RMSPE、MPE及泰爾不等係數(THEIL)；(2)變化方向誤差與趨勢變化誤差兩大方向來做預測比較。最後將算出的12期預測值與行政院主計處整體統計資料庫中所得到的失業率實際值利用預測評估準則做比較，結果發現一步預測法較多步預測法準確；而向量自迴歸模型(VAR)在大部份的預測期數上有較小的MAPE、RMSPE、MPE及THEIL值，因為此VAR模型考慮了在變數之間的共整合現象，有助於模型的預測，所以有較好預測的能力；反而是較複雜的ARIMA模型及轉換模型預測能力稍差一點。 / In this thesis, we plan to construct three time series models to forecast the Taiwan unemployment Rate. These time series models are ARIMA model、transfer function (TF) model and Vector Autoregressive (VAR) model. The data set consists of monthly observations for the period 75:1-87:12 for unemployment rate. We want to know if the data came from time trend model. First, we use multi-step forecasting and one-step forecasting to calculate 12 forecasted values from 88:01-88:12. Then We compare the prediction performance of these two methods by using：(1) MAPE、RMSPE、MPE and Theil’s Inequality Coefficient (THEIL)；(2) Direction of Change Error and trend Change Error etc. It is found that one-step forecasting is more correct than multi-step forecasting and the forecasting performance of VAR model is improved by explicitly taking account of cointegration between the variables in the model，so VAR model has lower MAPE、RMSPE、MPE and THEIL for most horizons. However，the more parsimonious ARIMA and transfer function models have higher MAPE、RMSPE、MPE for most horizons. 失業率 ARIMA模型轉換函數模型向量自迴歸模型共整合多步預測一步預測泰爾不等係數 unemployment rate ARIMA model transfer function model VAR cointegration multi-step forecasting one-step forecasting THEIL MAPE RMSPE MPE
29	Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work Buttar, Sarpreet Singh January 2019 (has links) Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals. Self-Adaptive Systems Self-Adaptation Architecture-Based Adaptation Autonomous Systems Cyber-Physical Systems CPS DeltaIoT IoT ActivFORMS MAPE-K Feedback Loop Runtime Uncertainties Adaptation Space Analysis Machine Learning Artificial Neural Network ANN Online Learning Deep Learning Online Supervised Learning Incremental Learning Classification Multi-Layer Perceptron MLP Computer Sciences Datavetenskap (datalogi) Control Engineering Reglerteknik Software Engineering Programvaruteknik

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