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I am looking for some kind of intelligent (I was thinking AI or Neural network) library that I can feed a list of historical data and this will predict the next sequence of outputs.
As an example I would like to feed the library the following figures 1,2,3,4,5
and based on this, it should predict the next sequence is 6,7,8,9,10 etc.
The inputs will be a lot more complex and contain much more information.
This will be used in a C# application.
If you have any recommendations or warning that will be great.
Thanks
EDIT
What I am trying to do i using historical sales data, predict what amount a specific client is most likely going to spend in the next period.
I do understand that there are dozens of external factors that can influence a clients purchases but for now I need to merely base it on the sales history and then plot a graph showing past sales and predicted sales.
826
Neural networks do not make any forecasts. Instead, they analyze price data and uncover opportunities. Using a neural network, you can make a trade decision based on thoroughly examined data, which is not necessarily the case when using traditional technical analysis methods.
Convolutional Neural Networks, or CNNs, were designed to map image data to an output variable. They have proven so effective that they are the go-to method for any type of prediction problem involving image data as an input.
AI algorithms are already predicting your future. In 2019, researchers at the University of Michigan revealed how a deep learning-based algorithm could predict the future location of a pedestrian and their gait.
Neural networks go a step beyond even traditional machine learning. A machine learning model can “learn” from data, making decisions based on what it learns. But a neural network can do more, rearranging its own algorithms in the process.
If you're looking for a .NET API, then I would recommend you try AForge.NET http://code.google.com/p/aforge/
If you just want to try various machine learning algorithms on a data set that you have at your disposal, then I would recommend that you play around with Weka; it's (relatively) easy to use and it implements a lot of ML/AI algorithms. Run multiple runs with different settings for each algorithm and try as many algorithms as you can. Most of them will have some predictive power and if you combine the right ones, then you might really get something useful.
135
If I understand your question correctly, you want to approximate and extrapolate an unknown function. In your example, you know the function values
f(0) = 1
f(1) = 2
f(2) = 3
f(3) = 4
f(4) = 5
A good approximation for these points would be f(x) = x+1, and that would yield f(5) = 6... as expected. The problem is, you can't solve this without knowledge about the function you want to extrapolate: Is it linear? Is it a polynomial? Is it smooth? Is it (approximately or exactly) cyclic? What is the range and domain of the function? The more you know about the function you want to extrapolate, the better your predictions will be.
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