ARGUS Enterprise 了解您的投資組合,可以控制風險並提高您獲利!
ARGUS Enterprise 整合了三大評估、資產管理解決方案,包含:ARGUS Valuation DCF、ARGUS Asset Management(舊名DYNA)與ARGUS Valuation - Capitalisation(舊名Circle),將其整合到單一綜合平台。 ARGUS產品汲取了豐富的經驗並結合大量的客戶反饋,增強了許多的功能,為客戶提供突破性的解決方案!
透過ARGUS產品您將可以結合專業知識、全球市場的知識、經驗和數據,透過正確的投資管理工具,您將可以迅速做出決策!
ARGUS Enterprise提供您認識和了解您的投資組合的詳細資料,讓您可以熟練地管理您的風險,讓您可以掌控自己的命運。
Unprecedented visibility into your portfolio
With advanced portfolio tools, create KPIs and dashboards customized for each user. Pivot portfolio results and analyze with filters and charts.
Intuitive and easy to use
New user interfaces for budgeting and other user interface enhancements make for faster analysis and less errors. Screens that are familiar to existing ARGUS users, allowing you to enter data the way you work.
Fastest deployment in the industry
ARGUS Enterprise can be installed at the desktop level for immediate property modeling or deployed on an enterprise level with enterprise level security and configuration. Our new deployment options will have you up and running as soon as you download and install the solution.
Interconnected System
Dynamic connections to Excel, combine with ARGUS Symphony to make ARGUS Enterprise a truly connected platform. Never before has the data been more accessible and usable bi-directionally with any asset management solution.
ARGUS Compatible
Leverage ARGUS DCF data. Along with the user interface, the data in ARGUS DCF files can be easily imported into ARGUS Enterprise. Trust ARGUS to create the highest level of compatibility with ARGUS DCF files.
ChaosHunter ®是一個獨立的軟體工具,旨在產生可讀的公式,您的數字數據模型的應用如下:
● 對金融市場生產買入/賣出信號
● 預測未來價值的時間序列,包括市場價格
● 建立科學數據模型
● 建立企業財務或銷售數據模型
● 預測實驗的結果等等
ChaosHunter要求您從電子表格或數據源輸入歷史文本文件或樣本數據。然後您需要選擇算術類別和您想使用的ChaosHunter的其他數學函數,之後它會產生的數值公式,讓您可以閱讀、理解、利用,甚至對外出售。ChaosHunter可用功能還包括神經網絡和混沌函數。
由ChaosHunter公式所產生的買入/賣出信號,可以被轉移到許多流行的交易平台,它使您能夠將您的模型與貿易數字的經紀公司。我們現在可以利用已經開發完成的界面,配合以下的平台就能讓您更輕鬆轉換ChaosHunter模型:
● NeuroShell® Trader Professional
● NeuroShell® DayTrader Professional
● Interactive Brokers Trader Workstation
● TradeStation®
● NinjaTrader®
● MetaTrader 4
● Wealth-Lab Pro®
● eSignal®
● Microsoft® Excel
Science and Trade Modeling
The ChaosHunter works by evolving formulas from basic building blocks - "atomic" functions like add, subtract, multiply, divide, sine, cosine, square root, etc. The user selects which of these functions will be in the pool of available functions, and the ChaosHunter evolves combinations that continually get better at solving the problem.
Solving the problem can mean predicting or classifying a time series for business and science users (primarily), or it can mean generating buy/sell signals for trading. Since the formulas that get generated are usually not esoteric like neural network formulas, you can show them to your boss, modify them, and insert them into other programs.
The ChaosHunter's atomic function set contains simple polynomials (e.g. a2 + b2), neurons, boolean functions (AND, OR, NOT), and many more. The neuron functions (if chosen) can combine to form unique neural net structures.
For our science and business users, you get models that have readable, understandable formulas to model the data. If the data is time series, it builds recurrent formulas necessary for most definitions of chaotic functions. Although it mostly predicts, you can classify as well if there are two classes, one of which can be described by positive numbers and the other by negative numbers.
The trading models it builds can be used standalone or in conjunction with NeuroShell Trader Professional or DayTrader Professional, and they are capable of investigating chaotic time series.
Traders can also fire the trading models the ChaosHunter makes from any number of trading products that you may have purchased before you found out how good NeuroShell Trader Professional is. The reason is that ChaosHunter makes formulas that can most likely be inserted into many of these systems. So you build the model (formula) based on text files exported from NeuroShell Trader or these other systems, then use the model in real time somewhere else. Of course, we have a much easier way to insert the models into NeuroShell...
此軟體試用版需付費使用,此成本可於購買時扣除,有需要請來信詢問
Concept Star 是最有實際經驗的分析系統結構法Interpretive Structural Modeling(ISM)的軟體,該軟體可在Windows系統上應用。他的設計是根據多年的諮詢領域的行政決策和組織問題解決的實踐經驗,Concept Star 是一款可以幫助你解決日常複雜問題的無價工具軟體。
Concept Star 易於使用,因為它的人機直觀介面和自動視覺顯示關係模型。這使得用戶能夠更方便地在地圖上確定路徑或線程的思想,以促進了解的情況和決策。
Concept Star 可用於作為個人桌面工具,也可以讓諮詢專業人員應用於幫助客戶解決問題上。公司組織支付了數千美元給ISM諮詢顧問。這款強而有利的工具不僅可以幫助您增加職涯的競爭優勢,也可以幫助您的組織規劃以及解決問題。
如同所有的功能強大的工具,為了以便使最有效地利用Concept Star您需要一些培訓。Concept Star Professional ISM Software and Training Package是ISM的套裝軟體,其中包括一本書(Structured Decision Making with ISM),提供深入培訓,有效地利用安全管理過程,除了軟體用戶手冊。這使得這個套裝軟體可以被任何人使用而不需要具有過往的知識領域或昂貴的專業培訓。
Data Mining with Cubist
Cubist 資料採礦軟體工具
資料探勘(Data mining)是指從組織的資料庫中萃取資料的過程,這些資料通常被用來洞察該組織的營運模式和預設未發生之結果,以支援使用者做決策。
Cubist是 RuleQuest Research 公司開發的建立預測模型的工具。其內建的規則算法可幫助建立預測模型的輸出值,並與See5/C5.0產品互補。例如,See5/C5.0可能依據其百分比將數據分類為“高”、“中等”或“低”,而Cubist將會是輸出一個數字,如“7.3”。
Cubist是一個功能強大的工具,Cubist模型比那些一般的技術,如多元線性回歸得可以到更好的結果,同時也比神經網絡分析更容易理解。
DEA SolverPro
近年來已經有很多使用資料包絡分析(Data Envelopment Analysis, DEA)來評估許多不同種類實體效益的多種應用,在很多不同背景的國家中從事不同的活動。一個原因是,因為經常有未知的複雜因素,很多活動牽扯到多次輸入和輸出之間的關係,DEA已經提供對解決這些情況的可能性。例子包括:在不同地理位置的美國空軍基地的維護活動,英國和威爾斯的警力分配,賽普勒斯和加拿 大分行的效能,和美國、英國以及法國的大學實施他們的教育和研究過程中的效率。借著各式各樣的輸入和輸出(包括把"社會"和"安全網"的支出當作輸入數 據,把各種方面的"生活品質"當做輸出資料),這些種類的應用延伸到用來評價城市、地區和國家的實施效率。
SAITECH Inc. 公司的 DEA-Solver-Pro 除了整合SBM (Non-Oriented SBM ) 模型並加上了 “Global RTS” 模型,也針對前幾版的功能做了修正。該模型是一個整合的模型,如圖A所示,該模型可以評估(1)所觀察到的整個期間的整體效率,(2)動態變化的週期(長期)效率(3)部門效率的動態變化及(4)分區麥式(Malmquist)指數。
這些特徵通過統一徑向(unifying radial)和非徑向模型考慮了輸入/輸出資料的差異,以及他們對於測量技術效率的相對重要性。使用EBM模型,DEA-Solver-Pro變得非常有效,甚至是那些帶有依賴資料和相關資料的案例。
更新介紹
In DEA, we often encounter negative data, e.g. financial data. In response to many requests, in this Version 15.0, we updated SBM models to cope with negative data. Three clusters are added under the Variable Returns-to-Scale (VRS) model. They are NegativeData_SBM, NegativeData_SBM_Max and NegativeData_Malmquist_SBM. Now, we can deal with positive and negative data in a single model. The efficiency scores are units invariant.
we introduced SBM_Bounded models as an extension of the SBM_Max models. In real world applications, there are cases where input-reduction and/or output expansion are restricted by some external constraints. For example, suppose that expansion of inpatient visits, as an output of hospitals, is bounded by 3% of the current value due to physical environments. Input reduction bound and output expansion bound differ for every input and everyoutput. The SBM_Bounded models handle these cases as the constraints to slacks which are obtained by SBM_Max models. Hence, the projection of inefficient DMUs thus obtained indicates more practical and useful improvement (Kaizen) plan. In Figure 14.0, we illustrate the model in the input side projection. DMUs A, B, C and D are on the efficient frontier while P and S are inefficient. P will be projected to R by the SBM_Max model, whereas the bounded area forces it to stop at Q. The bounded area is determined by Input 1 reduction rate and Input 2 reduction rate which are assumed to be common to all DMUs. DMU S is projected to T inside its bounded area and on the efficient frontier.
DTREG 是一款完美的建模工具,適用於商業建模或建立多種醫學資料模型,例如性別、人種、婚姻狀況等! 從一組數據值中提取有用信息的過程被稱為「數據挖掘」(data mining)。這些數據可以用來創建模型並做出預測。目前的技術已經發展出許多預測模型,而如何選擇和應用最好的模型則是一種藝術。
DTREG實現強大的已開發預測建模方法。你可以使用決策樹模型、支援向量機(Support Vector Machine, SVM)、基因表示規劃法(Gene Expression Programming)、符號回歸(Symbolic Regression),K-means 分群法(k-means clustering)、判別分析法(discriminant analysis)、線性回歸(Linear Regression models)和(Logistic Regression models)。DTREG也可以進行時間序列分析和預測。
Classification and Regression Trees. DTREG can build Classification Trees where the target variable being predicted is categorical and Regression Trees where the target variable is continuous like income or sales volume.
DTREG 提供多種強大的預測建模
Multilayer Perceptron Neural Networks
Probabilistic Neural Networks
General Regression Neural Networks
RBF Neural Networks
GMDH Polynomial Neural Networks
Cascade Correlation Neural Networks
Support Vector Machine (SVM)
Gene Expression Programming - Symbolic Regression
Decision Trees
TreeBoost — Boosted Decision Trees
Decision Tree Forests
K-Means Clustering
Linear Discriminant Analysis (LDA)
Linear Regression
Logistic Regression
fuzzyTECH 為模糊邏輯與類神經模糊的模擬軟體,能結合類神經學習,以模糊邏輯得到解決方案,提供表列式、矩陣與FTL文字格式,讓使用者定義語言變數與模糊規則,規劃所需的模糊邏輯系統,適合複雜的模糊邏輯系統設計等應用。
fuzzyTECH軟體可以用於創建模糊系統,然後用神經網路和樣本數據對所創建的模糊系統進行訓練,最後可以針對不同的單片機或DSP硬體平臺自動產生相應的程序代碼,直接應用到系統的調試中去。
fuzzyTECH可以解決在單片機或DSP的有限資源中如何編制高效的應用程序以實現復雜的模糊控制算法的問題。它降低了實現模糊控制算法的難度,也加快了設計模糊控制算法的速度。fuzzyTECH設計電阻爐控制系統的例子,說明了它的有效性。
fuzzyTECH 提供三種版本,符合使用者不同工作平台的需求:
通用型(General Purpose fuzzyTECH Edition)
使用者在電腦中進行模糊邏輯的應用,包含專業版與線上版,特點如:
圖形式的編輯器與分析器使用介面
提供不同邏輯演算,完整互動式除錯分析
產生C/JAVA/M Code等模糊邏輯控制的原始副程式
與VB/VBA或C++/MFC整合,或DLL/OLE模組
與MS Office Excel/Access整合
與Matlab/Simulink、InTouch、FactoryLink、Genesis、WinCC 等軟體整合
線上版除包含專業版功能,並提供 TCP/IP、IPX/SPX、DDE、RS-232與SDI(User Define Interface)
等即時遠端監控分析介面。
單晶片控制(fuzzyTECH MCU Pack for Embedded Control)
應用於一般控制、數位信號處理與特定的模糊邏輯處理器,產生模糊邏輯控制的最佳化組合語言程式或C語言程式。支援8051/80251、Motorola 68HC05/68HC08、Microchip、Mitsubishi、Intel MCS-96、Siemens、TMS-320與SAE81C99A等系列控制器。
fuzzyTECH MCU-HC05/08 Edition: Supports all microcontrollers of the 68HC05 and 68HC08
families from Motorola.
fuzzyTECH MCU-HC11/12 Edition: Supports all microcontrollers of the 68HC11xx and 68HC12xx
families from Motorola. Utilizes the special fuzzy logic instruction set of the HC12. RTRCD functionality included.
fuzzyTECH MCU-MP Edition: Supports all microcontroller families of Microchip Technologies Inc.
(PIC16C5X, PIC16CXX, PIC17CXX).
fuzzyTECH MCU-51 Edition: Supports all microcontrollers of the 8051 and 80251 families. Special libraries for
80517 included. RTRCD functionality included.
fuzzyTECH MCU-96 Edition: Supports all microcontrollers of the MCS-96 family from Intel (8096, 80C196, ...).
RTRCD functionality included.
fuzzyTECH MCU-166 Edition: Supports all microcontrollers of the C16x family from Siemens.
RTRCD functionality included.
fuzzyTECH MCU-320 Edition: Supports all digital signal processors (DSP) of the TMS-320 C2x/3x/4x and
5x families from Texas Instruments.
可程式控制(fuzzyTECH IA Editions for Industrial Automation)
提供模糊邏輯函數方塊,整合至PLC可程式控制器與程序控制系統,以歸屬函數、MIN-MAX推論、反模糊化方法與即時監控功能,完成模糊邏輯系統。支援Klockner-Moeller、Foxboro/Eckardt IAS等程序控制。
fuzzyTECH IA-S7 Edition: For Siemens SIMATIC S7™ (300 and 400) PLCs.
fuzzyTECH Professional Edition: For Structured Text by the I...