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                                      Forecaster XL

                                      Forecaster XL

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                                      Forecasting Excel 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.

                                      Neural networks inside Excel

                                      Forecaster XL allows you to use neural networks for forecasting and classifications directly inside your Excel worksheets. You can instantly apply the capabilities of neural network forecasting to XLS data, while retaining all the data manipulation tools in Excel.

                                      Reliable and easy Excel forecasting

                                      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.

                                      Automatic neural network design & training in Excel

                                      Forecaster XL is the only forecasting Excel add-in with an automatic neural network architecture and parameters selection. It gives you access to breakthrough algorithms for automatic data preprocessing and neural network preparation inside MS Excel. With these algorithms, you do not need to have any prior experience in statistics or artificial intelligence to exploit the power of neural networks in Excel. These algorithms make your data suitable for a neural network, select the most appropriate architecture and prepare the forecasting neural network for solving your problem.  
                                      Forecaster XL is ideal for anyone who has employed MS Excel for data analysis and wants to start using artificial intelligence to increase the accuracy of their forecasts. You can download a free 30-day trial now and start improving your results immediately.

                                      System Requirements: Windows 7,8,10; Excel 2007/2010/2013/2016/2019;
                                      32 bit only

                                      Reliable forecasting with neural networks

                                      High quality forecasting and classification due to employment of the latest achievements in artificial neural networks. The most reliable constructive algorithm available today is implemented in Alyuda Forecaster XL. The algorithm was carefully adopted and tuned for real-world applications. Better algorithms mean better forecasting. And Forecaster XL will bring it to you.

                                      Integration with Microsoft Excel

                                      By providing embedded support for Microsoft Excel, Alyuda allows you to use neural networks for forecasting directly inside your Excel worksheets. You can instantly apply forecasting capabilities of neural networks to your Excel data, while still retaining all of Excel’s data manipulation and formatting tools. Together, Forecaster XL and Excel give you the best of both worlds.

                                      Exceptional ease of use

                                      Forecaster XL was designed specially to give maximum comfort to Excel users with exploiting neural networks for forecasting inside Excel. All features are easily accessed from additional menu items and use only standard Excel interface for data manipulation.

                                      Forecasting in several clicks

                                      To get forecasting simply click "Create Network..." from Forecaster XL menu, select your input and target data and click "Train" to let Forecaster XL prepare the network for you. After this you need to just select or enter your new data and make one more click to get your forecasting ready. Furthermore, after the network is created you may save it and afterwards you need only to load it, enter new data and get your forecasting within seconds

                                      Hidden details of neural network theory

                                      Forecaster XL frees you from the need to learn details of neural network theory. It greatly simplifies the process of preparing a neural network needed for forecasting hiding from you any difficulty in neural network preparation and tuning.

                                      Automated network selection

                                      Forecaster XL automatically selects the most appropriate architecture for you forecasting problem.It runs a reliable constructive algorithm which finds the suitable network architecture automatically and saves you a lot of time.

                                      Detailed Reporting

                                      For those who want to have a detailed report about the created neural network and its structure as well as to inspect network performance during forecasting and data preprocessing, Forecaster XL delivers all needed information in separate Excel sheets. When creating a final report you may add training graph and error values of each iteration.

                                      General

                                      • Regression, classification types of forecasting
                                      • Time-series forecasting
                                      • Exceptional ease of use
                                      • Constructive neural network algorithms
                                      • Informative graphs and detailed reports
                                      • Online help system
                                      • Free technical support
                                      • Multi-language interface
                                      • Sample financial, marketing, real estate and scientific problems included

                                      Analyze Your Data

                                      • Number of inputs and records are limited only by Excel limitations
                                      • Automatic data analysis and pre-processing
                                      • Automatic categorical values encoding
                                      • Automatic numeric values scaling
                                      • Missing values handling for numeric values (removal and 4 substitution options)
                                      • Missing values handling for categorical values (removal and 3 substitution options)
                                      • Outliers handling (customizable outlier coefficient)
                                      • Manual min/max values specification to anticipate bigger values in data for forecasting
                                      • Automatic random and sequential dataset partition onto training and test sets
                                      • Detailed Data Analysis and Preprocessing Report

                                      Create Neural Network

                                      • Fully automated neural network design - a special state-of-the-art constructive algorithm automatically creates and trains the most suitable neural network to solve your problem.
                                      • Neural networks for time-series forecasting
                                      • Unique "Next Target" mode: combination of regression and time-series forecasting
                                      • Automatic generation of versatile stopping condition to stop network training
                                      • Generalization loss control (10 preset levels)
                                      • Retain and restore best network
                                      • Manual network retrain
                                      • Retrain network to get better results

                                      For experienced users

                                      • Manual stopping conditions (target error level, error improvement, correct classification rate, number of iterations)
                                      • Real-time control on training parameters (MSE, MAE, CCR, # of iterations).
                                      • Training Error Graph (network error by iteration)
                                      • Training Error Table (network error and error improvement by iteration)
                                      • Cost/Loss matrix

                                      Perform Performance Analysis and Forecasting

                                      • Actual vs Forecasted Graph
                                      • Actual vs Forecasted Scatter Plot
                                      • Input Importance Chart
                                      • Error deviation graph
                                      • Actual vs Forecasted Table with absolute and relative errors
                                      • Confusion matrix
                                      • Error distribution table
                                      • R-squared and correlation calculation for a network
                                      • Tolerance levels to quickly estimate overall forecasting quality
                                      • Single-point forecasting and bulk forecasting
                                      • Quick forecasting with already trained network

                                      Enjoy User Interface Extras

                                      • two convenient methods of data selection: by range and by column
                                      • complete color customization for reports and graphs
                                      • neural network autosave
                                      • load/save neural network from/to Excel worksheet/workbook
                                      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?
                                      What is a categorical column?
                                      Why Forecaster XL ignored some rows and columns?

                                      Network Preparation

                                      What is network training?
                                      What training algorithm Forecaster XL uses?
                                      How Forecaster XL determines neural network topology suitable for my problem?
                                      Why I cannot see MSE and absolute error in the network training report?
                                      What is “error change” in stopping conditions?
                                      How much time is required for network training?




                                      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.

                                      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.

                                      Why Forecaster XL ignored some rows and columns?
                                      This may happen if some of your columns or rows are unsuitable for neural network. For example, text or data/time data cannot be processed by neural network. Also, some your rows may have missing or invalid data; such rows will be ignored.
                                      To see which columns and rows were ignored look into Data Preprocessing Report.

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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 training algorithm Forecaster XL uses?
                                      Forecaster XL uses constructive algorithm to train network and select the network topology. This constructive algorithm is developed by Alyuda's Research Group and is capable of automatic selection and tuning of training parameters and network topology.
                                      How Forecaster XL determines neural network topology suitable for my problem?
                                      See What training algorithm Forecaster XL uses?

                                      What stopping conditions should I specify to improve forecasting quality?
                                      As the first step we recommend you using default settings that means your network is trained until error reduction is no longer possible. If forecasting error is still unacceptably high we recommend reducing MSE value, reducing the error change value and increasing number of iterations.

                                      Why I cannot see MSE and absolute error in the network training report?
                                      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 correct classification rate to let you know what percentage of data was recognized correctly.

                                      What is “error change” in stopping conditions?
                                      Error change specifies the error change during several last iterations. This parameter is useful for detection of situations when each new iteration has almost no influence on error and thus 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 setting 10 iterations, which is enough for most of problems. For certainty you can set up to 100 iterations.


                                      How much time is required for network 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.

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                                      For more information about product licensing, please visit Alyuda licensing.

                                      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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                                      We reserve the right to charge full price for products and services purchased at a discount if requested proof of status/age is not provided in a timely manner.

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                                      Forecaster XL

                                      Forecaster XL

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

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