為因應預測目的不同，有時需要各種預測水平{\rm (forecast horizon)} ，例如，月預測可供進料、生產、補貨及倉儲之參考，年預測則可作為產 能規畫、產品線規畫、投資決策等之準則。然而，預測結構卻會因水平的 不同而彼此相異，以致產生諸多預測值的矛盾。有鑑於此，本研究主要以 一簡單且具理論基礎的整合{\rm intergration)} 過程，解決預測值互相 矛盾的問題。由於年資料通常屬於連續性模式，月資料則多為季節性模式 ，兩者透過的轉移函數形態截然不同，而且在解釋變數的選取上更是迥異 ，因此，需要經由加權平均的整合，才能使月預測值的加總等於年預測值 。至於權數的決定則以離散程度為準則，由於年資料為月資料的加總，兩 者均值相差甚多，故以變異係數為測量離散情形的標準。本研究主要乃遵 循{\rm Box-Jenkins} 的模式建立法則，構建連續性轉移函數模式及季節 性、轉移函數模式，並加以整合調整。在實證分析中以台灣啤酒銷售量為 例說明預測流程，年銷量預測方面以國民所得為解釋變數; 月銷量預測方 面則以氣溫為解釋變數，最後以加權平均將兩者整合調整。

The trend seasonal demand pattern is encountered when both trend and seasonal influences are interactive. The problem of this research is to project the seasonal market sales using ice cream and fresh milk in Taiwan as examples. In order to improve the accuracy of forecast, two different methods are validated and the best forecasting method is selected based on the minimum Mean Square Error. In this study, we present two forecasting models used for evaluation to predict seasonal market sales of ice cream, fresh milk, and air conditioner in Taiwan. It includes Winters multiplicative seasonal trend model and the Decomposition method. Two different methods are validated and the best forecasting method is selected based on the minimum Mean Square Error. After the validation process, Winters multiplicative seasonal trend model is selected based on the minimum MSE, and the monthly sales forecast for the year of 2011 is conducted using the data(60 months). Number Cruncher Statistical System (NCSS) is used for analyzing the data which proves useful and powerful. In summary, the results demonstrate that Winters multiplicative seasonal trend model has the smallest mean square error in this case. Therefore, we conclude that both Winters multiplicative seasonal trend model and the Decomposition model are well fitted for forecasting the seasonal market sales. Yet, Winters multiplicative seasonal trend model is the better method to be used in this study since it generates the smallest mean square error (MSE) during the period of validation.

一般而言，防盜展示座是用於3C賣場、展覽會場、實體體驗店鋪、影音視聽設備販售商場、通訊賣場或店面等場所，而被使用的商品更包含手機、影音視聽設備、照相機、攝影機、平板電腦、筆記型電腦、通訊設備等3C商品，當展覽會場或銷售商場將3C產品安裝於展示座之上，即可安全的提供商品陳列，而這樣提供一個安全性的商品銷售的產品，便是防盜展示座與3C產品間密不可分的說明。換言之，當3C產品的銷售量或生產量達到一定程度時，防盜展示座的需求量也會隨之上升。因此，本研究擬欲透過3C產品的生產與銷售概況，據以進一步瞭解防盜展示座的產業發展，故本研究擬針對3C產品之營運現況及產銷現況進行探究，其研究結果顯示： 1. 防盜展示座的產業預期具有正向的發展； 2. 3C產品之產銷現況受季節因素的影響； 3. 3C產品之營運現況是呈現成長趨勢； 4. 防盜展示座預期可能會受季節因素影響。

近年來，能源商品的價格隨著國際政治情勢、國際金融環境以及景氣循環的影響產生劇烈波動，基於避險的需求，衍生性商品交易量也逐漸增加。然而，在評價能源衍生性商品的過程中，即期價格動態模型的選擇對於訂價與避險的結果有著顯著的影響，如何選擇一個適當的動態模型以評價能源商品便成為本文研究的目標。在指數與股價選擇權的評價模型中，大多以Black and Scholes (1973)提出的選擇權評價模型作為基礎，但Black-Scholes模型是否適用於評價能源市場的選擇權價格卻是有待商榷。Schwartz (1997)提出以均數回歸模型 (Mean Reversion Model)描述能源即期價格，發現比Black-Scholes模型中所假設的即期價格動態模型更能描述能源市場即期價格的波動。本研究也考慮能源市場遇到重大事件而造成即期價格產生劇烈波動的情況，因此在模型中加入跳躍項以捕捉價格跳躍的現象。另外，能源商品的需求與季節變化有高度相關性，因此本文亦考量即期價格的變動會受到季節性的變動影響，在模型中加入季節性函數，以補捉季節性的價格變化。基於前述模型考量，本研究在各種描述能源商品即期價格特性的動態模型之下，推導各個模型的期貨選擇權定價公式，進一步測試各模型在金融風暴與非金融風暴期間的訂價誤差與避險誤差，以提供投資人或避險需求者於原油期貨選擇權模型選用上之參考。 / In recent years, the price of energy commodities has fluctuated with the international political situation and the international financial environment. For the sake of hedging demands, the trading volume of derivatives has been gradually increasing. In the process of valuation of energy derivatives, choices of the spot price dynamics model have a significant impact on pricing and hedging. Therefore, how to choose an appropriate dynamic model to evaluate the energy commodities has been main purpose of this study. Two main models are tested in this paper. One is the option pricing model supposed by Black and Scholes (1973), and another is the mean reversion model supposed by Schwartz (1997). This study also considered the volatility of the spot price in the energy market in case of major events, so the researcher adds the jump to explore the mean reversion model. In addition, the demand for energy commodities is highly correlated with seasonal variations. The vibration of spot price often affected by the seasonal variations is considered in the research. Therefore, the researchers also take the seasonal function into the research to capture the seasonal price changes. Based on considerations described above, the pricing formula for each model of futures option is evaluated in the research. The researcher further tests the pricing errors and hedging errors of each model during the financial crises and non-financial crises in order to provide the investors and hedging demanders with some suggestions about selecting oil futures option models.

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本文的論述態度與研究範圍上，係以定期勞動契約為軸心，基於勞基法第九條乃是定期勞動契約的最根本的法源，所以本文擬從勞基法第九條定期契約的定義這個切入點著手，具備什麼樣的要素是屬於定期勞動契約，具備什麼樣的要素又是屬於不定期勞動契約，這個要素應該如何去作解釋；於定期勞動契約的定義之後，接著介紹目前定期勞動契約的類型，除了勞基法所規範的類型外，特別法上或實務上的類型亦在觀察之列。而區別定期勞動契約與不定期勞動契約的實益何在，法律關係之權利義務有何不同，本文也作一闡述，進而審視目前的實務操作的結果會是如何，到底合不合理，最後借鏡外國的立法例，為求配合現實的經濟環境，在立法論上或解釋論上來提出結論。 本論文摘要說明如下： 第一章為緒論，包括本論文之研究動機、研究目的及研究方法加以說明，藉以瞭解本論文題目的產生背景，並稍加說明各章節之內容。 第二章為我國勞動基準法上定期契約的定義，將從立法背景與理由切入，解析勞動基準法第九條，何謂「繼續性」，並舉出主管行政機關的判斷標準以及學說見解，來對定期契約的認定與種類作闡釋與論述。除此之外並對與定期勞動契約相關的議題：外籍勞工、國防役男、國會助理、公營移轉民營條例之定期人員、試用期等來作解析。 第三章為定期勞動契約的權利義務，從各個角度去剖析定期勞動契約雙方當事人的法律關係，包括法定更新、連續性定期契約、資遣、退休、年資累計等問題。 第四章為整理我國實務見解，在此章將對搜尋所得的有關定期契約判決作一分析整理，試圖勾勒出法院對於定期契約判斷標準的模式與界線。 第五章為介紹德國立法例，所謂他山之石，可以攻錯，德國關於定期勞動契約的制訂法演變共有三階段，而這其中的關鍵毋寧說是經濟因素的變遷，使得制訂法上因循變異，其日漸放寬定期勞動契約成立的限制，相較於我國，帶給我們何種啟示為探討，藉以提供我國法制比較之參考。 第六章為結論，本章將針對前面各章所得之結論，進行歸納與處理，總結本文所述，提出結論與建議。

本論文主要研究以類神經網路模式預測季節性時間序列之有效性。利用適 當地建構樣本訓練集,網路經訓練後可作為季節性時間序列之預測工具。 文中亦提出移動學習法以期提高預測之準確度。並以台灣地區每季進口商 品與勞務總值則作為實證之研究。此季節性時間序列因受離群值之影響而 增加其預測困難度。實證結果顯示類神經網路模式之預測表現較傳統之統 計方法優異,即使此序列受到離群值之干擾。 / We investigate the effectiveness of neural networks for predicting the future behavior of seasonal time series. Utilizing the training set constructed properly, we can train the network who can be used to predict the future of seasonal time series. A shifting-learning method is also employed in order to obtained a better forecasting performance. The quarterly imports of goods and services of Taiwan between the first quarter of 1968 and the fourth quarter of 1990 are studied in the research. The series are contaminated with outliers, which will increase the difficulty of forecasting. Empirical results exhibit that neural networks model free approach have better prediction performance than the classical Box-Jenkins approach, even the series are contaminated with outliers.

博士(学術) / 甲第508号 / 243p / Hitotsubashi University(一橋大学)

本文將模糊理論的觀念，應用到時間數列分析上。研究重點包括模糊自相似度的定義與度量，模糊自迴歸係數的分析，模糊相似度辨識與自迴歸階次認定、模糊時間數列模式建構與預測等。我們首先給定模糊時間數列模式的概念與一些重要性質。接著提出模糊相似度的定義與度量，以及模式建構的流程。經由系統性的模擬與分析，我們建立階次認定的演算法則與認定程序。藉著詳細的演算比較這些類型的模糊時間數列。並以模糊關係方程式推導，提出合適的模糊時間數列模式建構方法。並利用提出的方法對台灣的景氣對策信號，及台灣結婚率建立模糊時間數列模式。最後，使用所建構的模糊時間數列模式對未來進行預測，以驗證所建構模糊時間數列模式的效率性與實用性。 / In modeling a time series the accuracy of various model constructions and forecasting techniques, certain rules and models are adhered to. Traditional methods on the model construction for a time series are based on the researchers' experience by choosing a "good" model, which will satisfactorily explain its dynamic behavior, from a model-base. But a fundamental question that often arises is: does the data exhibit the real case honestly? In this research we show how fuzzy time series construction be applied for this purpose. An order detection process for fuzzy time series is presented. Simulation has been used extensively to explore general properties of statistical procedures, and the approach is particularly useful in fuzzy time series construction. Statistical strategies typically consist of sequences of rules used repeatedly on the same data set. This paper is organized as follows: In Chapter 2 we will discuss about the definition of fuzzy time series as well as certain important properties. In Chapter 3, We use the similarity comparison process to decide the order of a fuzzy time series. Simulations and analysis with the results about various types of autocorrelation are experienced in Chapter 4. Finally, we apply our methods to three empirical examples, Taiwan business cycle index, marriage rate and numbers of students enrollment in Chapter 5. Chapter 6 is the conclusion and the discussion of future researches.

Schwarz (1997)提出均數回復過程（Mean-Reverting Process, MR）捕捉能源價格的動態過程，而Lucia and Schwarz (2002)將此模型結合確定季節性函數，並推導出期貨價格封閉解。然而，能源價格常會因為未預期事件的發生而產生大幅度的變動，為了描述價格跳躍的現象，Clewlow and Strickland (2000)延伸Schwarz的模型提出均數回復跳躍擴散模型（Mean-reverting jump diffusion process, MRJD），此模型除了保留均數回復模型對能源價格會回復至長期水準的描述外，再加上跳躍項來描述價格的異常變動。而Cartea and Figueroa (2005)則同時考慮季節性和跳躍因子，並推導出期貨價格封閉解。另外，雖然台灣目前並非京都議定書所規範的國家，但環境保護是未來的趨勢，故在衡量能源產業財務風險時，除了考慮相關原料和產品，應考慮碳權交易之影響。為了探討財務風險管理在能源產業之應用，本文以煉油廠為例，將其表示成特定期貨部位的投資組合，並透過計算投資組合風險值來衡量煉油廠的財務風險。文中使用結合季節性的均數回復過程、均數回復跳躍擴散過程進行模型配適。實證結果顯示，均數回復跳躍擴散模型在回溯測試下表現最佳；另外，考慮碳權交易後會使得煉油廠的財務風險上升。 / Schwarz (1997) proposes the mean-reverting process (MR) to model energy spot price dynamics, and Lucia and Schwarz (2002) extend this model by including mean reversion and a deterministic seasonality. This model can capture the mean-reversion of energy price, but fail to account for the huge and non-negligible price movement in the market. Clewlow and Strickland (2000) extend Schwarz’s model to mean-reverting jump diffusion process (MRJD). Cartea and Figueroa (2005) present a model which captures the most importance characteristics of energy spot prices such as mean reversion, jumps and seasonality, and provide a closed-form solution for the forward. Although Taiwan is not the member of Kyoto Protocol, but Environmental Protection is a trend in the future. In order to measure the financial risk induced by energy industries, we should consider the effect of emission trading. In this paper, we discuss the implication of financial risk management in energy industries by analyzing the exposure of refinery which represented certain energy futures portfolios. We use MR and MRJD process with seasonality to model energy spot price dynamics, and calibrate the parameters to historical data. And, we consider the interaction of all of positions and calculate the Value-at-Risk of portfolios. The results show that among various approaches the MRJD presents more efficient results in back-testing, and emission trading poses additional risk factors which will increase the financial risk for refineries.