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							 Intelligent 
							Forecaster offers 
							a variety of functions to analyze and transform time 
							series. The functionality includes a variety of time 
							series graphs, a variety of seasonal plots, 
							autocorrelation plots, and PQ-scatter plots. In 
							addition to descriptive time series statistics the 
							functions include automatic tests of stationary 
							series incl. augmented Dickey-Fuller tests, 
							seasonality tests, trend-tests, automatic reporting 
							facilities.  It further offers functions to 
							analyze multiple series ... [find out 
							more] | 
							
							Functionality for cleaning data allows the 
							imputation of missing values and zero values and to 
							create dummy time series of outlier values. In 
							addition explanatory time series of binary or 
							integer dummies can be created manually or 
							automatically from iCalendar data.  [find out more]
							
 
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							 Intelligent 
							Forecaster offers a variety of forecasting methods, 
							including neural networks (with various activations 
							functions, hidden layer dimensions, number of 
							neurons,  and training algorithms), support 
							vector regression (with various kernel functions and 
							flexible parameter settings), and statistical 
							benchmarks of Naive methods, Averages and 
							Exponential Smoothing (with various forms of 
							Initializations, Parameter Optimizers, and parameter 
							ranges) . 
							
							 [find out more]  | 
						
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							The software allows flexible analysis reports of 
							multiple error measures (incl. MAE, MSE, MAPE, SMAPE, 
							MASE, AIC, BIC etc.) for multiple forecasting 
							horizons across fixed forecasting horizon, rolling 
							origin evaluation across a set of multiple error 
							measures using error tables and box plots 
							
							[find out more] 
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							 A 
							BATCH version of the software Intelligent Forecaster 
							allows  parameter file and command-line 
							parameter driven, fully automatic BATCH forecasting 
							of a large number of time series. Performance on 
							standard INTEL-based Servers and Virtual Servers 
							allows the prediction  of up to 10,000,000 
							neural network ensembles and time series per hour. [find out more] 
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