課程大綱：序列、時間序列與預(yù)測培訓(xùn)
Sequences and Prediction
Hi Learners and welcome to this course on sequences and prediction!
In this course we'll take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time,
like the temperature on a particular day, or the number of visitors to your web site.
We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!
Deep Neural Networks for Time Series
Having explored time series and some of the common attributes of time series such as trend and seasonality,
and then having used statistical methods for projection,
let's now begin to teach neural networks to recognize and predict on time series!
Recurrent Neural Networks for Time Series
Recurrent Neural networks and Long Short Term Memory networks are really useful to classify and predict on sequential data.
This week we'll explore using them with time series...
Real-world time series data
On top of DNNs and RNNs, let's also add convolutions,
and then put it all together using a real-world data series -- one which measures sunspot activity over hundreds of years, and see if we can predict using it.
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