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                                    Forecaster

                                    Forecaster

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                                    Forecasting software

                                    Alyuda Forecaster XL is a forecasting Excel add-in, based on neural networks. It is the obvious choice for users, who need a reliable and easy-to-learn forecasting neural network tool embedded into the familiar MS Excel framework.

                                    Easy start with neural networks

                                    Forecaster XL is designed specifically to save you time and money. It is reliable due to the implementation of the cutting-edge advances in Artificial Intelligence and ANN combined with proven neural network forecasting techniques. This forecasting neural network add-in is extremely easy to use for non-technical people. You only need to show your data and click just one button to prepare a forecasting neural network in Excel tailored to solve your specific problem.

                                    Three modes of Alyuda Forecaster

                                    Offering three different interface modes, Forecaster provides maximum comfort for users with different levels of knowledge in neural networks.

                                    Basic Mode: those users who receive the forecasting results values the quickest way possible will enjoy the Basic Mode. This mode has the minimum number of steps, does not display any intermediate results and requires minimum actions from the user.

                                    Standard Mode: those users who are just beginning to use the power of neural networks in their forecasting will feel more comfortable when starting with the Standard Mode. This mode provides non-technical explanations and settings and requires no prior knowledge of neural networks.

                                    Expert Mode: Seasoned neural network experts can use this mode to fully exploit all the power of neural networks and genetic algorithms. This mode provides fully customizable data analysis, preprocessing, network design, architecture search, training algorithm and parameter selection, as well as visual control over network errors during network training.

                                    Comfort and Benefits

                                    Alyuda Forecaster is specifically designed to solve major problems encountered while using a neural network for forecasting purposes. In addition to the feature benefits you will:
                                    1. save your time - no need to spend your time on learning the neural network theory;
                                    2. automatically prepare your data - your data will be analyzed and converted to make it suitable for a neural network;
                                    3. automatically accomplish the best neural network.

                                    Forecasting


                                    Selecting Network File Forecasting Report Preview
                                     

                                    Data Analisys and Preprocessing


                                    Locating Input Data File Selecting Target Column Data Analisys Details
                                     

                                    Standard Mode - Neural Network Preparation


                                    Neural Network Preparation Network Preparation Results Network Training Details
                                     

                                    Expert Mode - Neural Network Preparation


                                    Selecting Network Topology Selecting Training Parameters Network Training

                                    Reliable forecasting

                                    High quality forecasting and classification due to employment of the latest achievements in artificial neural networks. Only proven techniques and algorithms are implemented in Alyuda Forecaster. The latest technology improvements are carefully selected and tested by Alyuda Research experts on a comprehensive set of real-world applications. Better technology means better forecasting. And Alyuda Forecaster will bring it to you.

                                    Exceptional ease of use with Wizard-like interface

                                    Comparing to other neural network packages, Alyuda Forecaster is one step ahead in giving its users comfort and freeing them from necessity to learn details of neural network theory.
                                    The Wizard-like interface greatly simplifies the process of creating a neural network needed for forecasting. The extensive context-sensitive help is always available.

                                    Basic, Standard and Expert Mode

                                    Users who want to get forecast values the quickest way possible will enjoy Basic Mode. It has the minimum number of steps, does not display intermediate results and requires minimum actions from a user. Standard Mode is beneficial for users who just start using the power of neural networks for their forecasting. When these users gain confidence or want to use more advanced features, the Expert Mode full-packed with features is just a click away.

                                    Forecasting in several clicks

                                    To start forecasting simply double click on previously created neural network file listed in Windows Explorer, enter your input data and make one more click to get your forecasting ready. Pure Forecasting Mode eliminates spare steps and provides a convenient interface for forecasting only.

                                    Automated network selection

                                    Alyuda Forecaster will help you in selecting the most appropriate model for you forecasting. It runs an optimization process which finds suitable model automatically and saves you a lot of time.

                                    A lot of available training algorithms and parameters

                                    The most efficient algorithms, like Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, variations of Back-Propagation, are available for neural network training. Wizard can automatically select the most suitable algorithm for your case or you can experiment with algorithms and their parameters yourself.

                                    Great capabilities of automatic and manual data preparation

                                    You data will be automatically analyzed and transformed to be suitable for neural network. If you have special knowledge of how your data should be preprocessed, just switch to Expert Mode and you will have a full control of every detail of data preprocessing.

                                    Comprehensive Reporting

                                    Need to report your forecasting activity or save results of your work in presentable format? No problem. Alyuda Forecaster will allow you to customize your reports as well as save them as Excel, HTML or TXT.

                                    General

                                    • Wizard-like interface
                                    • Three interface modes: Basic, Standard and Expert
                                    • Automatic and manual data preprocessing
                                    • Automatic and manual selection of neural network architecture and training parameters
                                    • Detailed reporting
                                    • Online help system
                                    • Free technical support
                                    • Sample financial, marketing, real estate and scientific problems included

                                    Analyze and Pre-process Your Data

                                    • Input dataset size is limited only by the hardware of the computer
                                    • Import popular ASCII file formats (CSV, TXT, PRN) with automatic recognition of delimiter and column headers
                                    • Import Excel files
                                    • Automatic Date/Time values encoding
                                    • Automatic categorical values encoding
                                    • Automatic numeric values scaling
                                    • Missing values handling (removal and 4 substitution options)
                                    • Outliers handling (customizable outlier coefficient)
                                    • Automatic recognition of data entry errors (wrong type values)
                                    • Automatic and manual column type identification (numeric, categorical, date, time, text)
                                    • Automatic random dataset division onto training, validation and test sets
                                    • Detailed Data Analysis Report

                                    For experienced users

                                    • Manual dataset division onto training, validation and test sets (random or sequential)
                                    • Visual representation of data anomalies in Data Analysis Details window
                                    • Ability to accept/ignore rows and columns manually
                                    • Manual min/max values specification to anticipate bigger values in future data for forecasting
                                    • Detailed Preprocessing Report

                                    Design Suitable Neural Network

                                    • Fully automated neural network design - automatically search for the best architecture and train the most suitable neural network to solve your problem
                                    • Three timesaving methods of neural network architecture search
                                    • Exhaustive architecture search with customizable parameters
                                    • Genetic algorithms architecture search with customizable parameters
                                    • Automatic training algorithm selection

                                    For experienced users

                                    • Manual selection of training algorithms: Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, Incremental and Batch Back-Propagation
                                    • Manual architecture specification (supported network type: multi-layer perceptron)
                                    • Automatic adjustment of learning rate and momentum during training for Back-Propagation algorithm

                                    Control Network Training Process

                                    • Automatic generation of versatile stopping condition to stop network training
                                    • Non-technical presentation of neural network-related options in Standard mode
                                    • Real-time training error graph (absolute error by iteration)
                                    • Early-stopping on generalization loss (10 preset levels)
                                    • Retain and restore best network
                                    • Automatic network retrains and selection of the best network among retrains
                                    • Manual network retrain (4 options)

                                    For experienced users

                                    • Manual stopping conditions (target error level, error improvement, correct classification rate, number of iterations)
                                    • Real-time control on training parameters: errors (MSE, MAE, CCR) on training and validation set, error improvement, training speed, # of iterations

                                    Perform Performance Analysis and Forecasting

                                    • Estimated forecasting error
                                    • Actual vs Forecasted Table with absolute and relative errors
                                    • Single point forecast and bulk forecasting
                                    • Quick forecast with already trained network

                                    Enjoy User Interface Extras

                                    • Detailed explanations on every step
                                    • Customizable reports (with preview and printing capabilities)
                                    • Reports export to HTML and XLS
                                    • Save/Load neural network
                                    General

                                    Where can I get additional information about neural networks?
                                    How could I improve things to get better forecasting?
                                    When neural networks are a bad choice for my forecasting?

                                    Data Analysis and Preprocessing

                                    How much historical data do I need?
                                    Why some columns are grayed after Data Analysis and cannot be selected as targets?
                                    What is a categorical column?
                                    How can I see which records and columns were removed from analysis?
                                    What is your algorithm of removing misplaced data?

                                    Network Preparation

                                    What is network training?
                                    What is the best training algorithm for my problem?
                                    Why the absolute error became disabled during the Network Preparation step?
                                    What is “minimum improvement in error”?
                                    How could I speed-up network selection?
                                    How much hidden layers and units do I need?
                                    How much time is required for training?
                                    Can I change the network parameters after training?

                                    Forecasting and Reporting

                                    How could I forecast several values at once without entering them manually?
                                    How could I change report format?
                                     
                                     


                                    General

                                    Where can I get additional information about neural networks?
                                    There is a good introductory book written by Kevin Gurney and available online at: http://www.shef.ac.uk/psychology/gurney/notes/index.html

                                    You can also try Dr. Leslie Smith’s brief online introduction to neural networks packed with pictures and examples at: http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html.

                                    A good introductory book for managers and business analysts is:
                                    Bigus, J.P. (1996), Data Mining with Neural Networks: Solving Business Problems--from Application Development to Decision Support, NY: McGraw-Hill.

                                    For engineers and technically-minded people we’d recommend to start with: Fausett, L. (1994), Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Englewood Cliffs, NJ: Prentice Hall.

                                    For financial specialists, bankers and traders we recommend starting with: E. Michael Azoff (1994). Neural Network Time Series: Forecasting of Financial Markets NY: John Wiley and Sons, Inc.

                                    How could I improve things to get better forecasting?
                                    You have two ways to improve results:
                                    1) improve you input data (for more information please read Preparing Data Sets section in Advanced Issues chapter)
                                    2) improve network topology selection and network training (for more information please read Selecting Network Topology and Training Network sections in Advanced Issues chapter).

                                    When neural networks are a bad choice for my forecasting?
                                    Neural networks cannot create or digest the information that is not contained in your data. To properly train a neural network you need to have a lot of data. You data should contain input parameters (signals, attributes, correlated values) that affect the target value. Change of input parameters should lead to change of target one.
                                    So, if you have small amount of historical data or if you do not know, which parameters influence your target value, better use some other forecasting method.
                                    In addition, there exist some problems that in principle cannot be solved by neural networks. Do not use neural networks (as well as other numerical methods) for problems like:

                                    • predicting random or pseudo-random numbers, like lottery numbers
                                    • forecasting cash flow, volumes of sales, etc. if your business isn’t stable and your market situation often changes dramatically.
                                    • any problem where historical data have no use due to unbiased, rapid and significant changes in the problem environment.

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                                    Data Analysis and Preprocessing

                                    How much historical data do I need?
                                    You definitely need to have more records in the training subset than the total number of input columns.
                                    The number of records needed for training depends on the complexity of your problem and amount of noise in your data. There are no exact rules. Typically, it’s recommended to have at least 10 times as many records for training as input columns.
                                    This may not be enough for problems with subtle and complex dependencies in data. Try to add more data if your network has poor results.

                                    Why some columns are grayed after Data Analysis and cannot be selected as targets?
                                    The grayed columns cannot be converted for the use with neural networks. These are typically text columns, data/time columns, or columns that have a lot of misplaced or missing data.
                                    You may control the process of column accepting/ignoring in Expert Mode:

                                    • The handling of missing and misplaced values can be specified at Data Analysis step.
                                    • columns identified as containing text can be considered as categorical by ordering Alyuda Forecaster to accept them.
                                    • The date/time columns may be used only after specifying the required periodicity of their encoding.

                                    What is a categorical column?
                                    Each value of a categorical column represents a certain category. For example, categorical is a column that contains only “Male” or “Female” as its values. Typically, the number of different values in a categorical column is much less than the number of records.
                                    Categorical data should be encoded in a special way to be suitable for a neural network.
                                    You may manually mark a column as categorical in Expert Mode (using Details button at Data Analysis Progress step). This feature may be beneficial for some cases. For example, your data has a column “Model” that has values “1”, “2”, “3”. By default, this column will be considered as a numeric, but it will be more beneficial to encode it as a categorical one.

                                    How can I see which records and columns were removed from analysis?
                                    During the Data Analysis step click the “Details” button and you will see your data with grayed columns and rows. All colored cells will be removed from further use. In the Details window you may also see a reason of removing a record. The cells containing missing, misplaced data or outliers are painted with different colors. You can control this process in Expert Mode. In this mode you can set your preferences for data analysis.

                                    What is your algorithm of removing misplaced data?
                                    If all data in one of your columns contain numbers with the exception of several values, Wizard will identify this column as numeric. These several values will be identified as misplaced and records containing them will be removed. The same is true for other types of columns.
                                    The main question is this algorithm is “How many these “several” can be?” If you suspect that your data may have misplaced values, you need to give the Wizard a clue of how much misplaced values can be in your noisiest column. You can do it during Data Analysis step in Expert Mode.
                                    There is no misplaced data handling in Standard Mode. All columns are considered to be free of misplaced values, and if a numeric column contains at least one text value, it will be considered a text one.

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                                    Network Preparation

                                    What is network training?
                                    Network training means adjusting neural network weights. During training the network analyzes the data you have provided and changes weights between network units to reflect dependencies found in your data.

                                    What is the best training algorithm for my problem?
                                    If your data have up to 10 input columns, the best training algorithm will be Levenberg-Marquardt. It is fast and quite reliable.
                                    If you have a data set with hundreds of thousands of records and more, we recommend trying Incremental Back Propagation first.
                                    For all other cases it fully depends on your type of problem and dependencies inside your data. We recommend to start with Conjugate Gradient Descent and then try Quick Propagation and as the last step Batch Back Propagation or Incremental Back Propagation.

                                    Why the absolute error became disabled during the Network Preparation step?
                                    When your target column is not numeric, it is hard to define unambiguously what the absolute error is. For such cases it is better to use only relative errors, which is enough to completely control the training process.
                                    In Expert Mode you may use CCR (Correct Classification Rate) instead of error threshold definition.

                                    What is “minimum improvement in error”?
                                    Minimum improvement in error specifies the minimum error change during each iteration (or during several last iterations). This parameter is useful for detection of situations where the network cannot further improve its performance and training should be stopped to save time.
                                    Although one should be careful with this parameter because in certain cases the error can be decreased after a lot of “motionless” iterations. It’s impossible to automatically detect such cases. We recommend to set 10 iterations, which is enough for most of of problems. For certainty you can set up to 100 iterations.
                                    How much time is required for network selection?
                                    The time required for network selection depends on the number of inputs, amount of data, complexity of the task and capability of your computer. The network selection can last from several seconds to several hours.

                                    How could I speed-up network selection?
                                    The first way is to select the “Rough search” method, which is the quickest one but does not guarantee the best results.
                                    The second way is to specify the minimum and maximum number of hidden units your problem may require (Expert Mode only). This way requires some experience in neural networks and at least approximate estimation of problem complexity.

                                    How much hidden layers and units do I need?
                                    In our experience, the majority of problems (ca. 80%) have a good solution with 1 hidden layer, another part (ca. 20%) has a good solution with 2 layers, and only 1-2% of problems need 3 layers or more. More than two hidden layers are typically beneficial only for special problems, such as ZIP code recognition.

                                     

                                    If you have a small number of hidden units you will get a big error during forecasting, because there is not enough power to find and encode dependencies of your data. If you have a big number of hidden units neural network tends to memorize your data rather than encode dependencies and this will also lead to a big error during forecasting.

                                    For majority of problems, there is only one way to find the best number of hidden units: train several networks with different number of hidden units and find the best network by comparing forecasting errors on testing subset.
                                    Alyuda Forecaster uses several proprietary algorithms of searching for the best number of hidden units. These algorithms, in out point of view, strike the best balance between the need to reduce the search time and to find the best variant.
                                    To search among all variants you may start exhaustive search, but be prepared to wait a long time.

                                    How much time is required for training?
                                    The time required for network training depends on the number of inputs, number of hidden units, amount of data, complexity of the task and capability of your computer. Complete network training can continue from several seconds to several hours.

                                    Can I change the network parameters after training?
                                    Yes, you can press the “Back” button and change network parameters, but you will need to train your network again. The previous network will be lost unless you saved it in a file.

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                                    Forecasting and Reporting

                                    How could I forecast several values at once without entering them manually?
                                    Alyuda Forecaster doesn’t have this feature.

                                    How could I change report format?
                                    During the Reporting step press the “Show Report” button. You will see report preview. Click “Save As…” in the “File” menu and select desired format in the “Save as type” dropdown list.

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                                    The 30-day Trial Policy

                                    Alyuda NeuroIntelligence, Alyuda Forecaster XL and Alyuda Forecaster can be downloaded and used as free trial versions during a 30-day period. Alyuda NeuroFusion provides with detailed Help file which enables the user to easily understand the library. Once you have purchased one of those products, no refunds will be issued for your order(s).

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                                    Forecaster

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                                    Please note: you are eligible for free technical support during 30-day evaluation of Alyuda Forecaster.

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