Time Series:

A sequence of vectors (or scalars) which depend on time t. In this lecture we will deal exclusively with scalars: { x ( t 0 ), x ( t 1 ), · · · x ( ti − 1 ), x ( ti ), x ( t i+1 ), · · · }

It’s the output of some process P that we are interested in:

P-> x(t)

Time series are the focus of several overlapping disciplines:

Information Theory deals with describing the stochastic time series.

Dynamical Systems Theory deals with describing and manipulating mostly non-linear deterministic time series.

Digital Signal Processing deals with describing and manipulating mostly linear time series, both deterministic and stochastic.

We will use concepts from all three.

Possible Types of Processing:

predict future values of x[t]

classify a series into one of a few classes “price will go up” “the price will go down” — sell now “no change”

describe a series using a few parameter values of some model

transform one time series into another

I think that you've got the basic idea: a "sliding window" approach where a network is trained to use the last k values of a series (Tn-k ... Tn-1) to predict the current value (Tn).

There are a lot of ways you can do this, however. For example:

How big should that window be?

Should the data be preprocessed in any way (e.g. to remove outliers)?

What network configuration (e.g. # of hidden nodes, # of layers) and algorithm should be used?

Often people end up figuring out the best way to learn from their particular data by trial and error.

I highly recommend looking at the __neural_forecasting__ website, which contains tons of information on neural network forecasting competitions. The __Motivations__ page is especially useful.

Watch this video to learn about Neural Networks:

**Learn how to use Time Series Prediction via Neural Networks with this Neural Networks Tutorial.**