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Introduction to time series

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Sequences, Time Series and Prediction

deeplearning.ai

4.6 (275 calificaciones) | 6.7K estudiantes inscritos

Curso 4 de 4 en TensorFlow in Practice Programa Especializado

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Destrezas que aprenderás

Forecasting, Machine Learning, Tensorflow, Time Series, prediction

Revisiones

4.6 (275 calificaciones)

5 stars
202 ratings
4 stars
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3 stars
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1 star
9 ratings

GG

Aug 05, 2019

Fantastic course, starting from basic fundamentals of statistical forecasting to using Convolutional neural networks. I will use my learnings directly to my job.

SD

Jul 31, 2019

Quite a good light-weighted course on Time Series and Prediction. It was quite helpful for people like me who are seeking ways to implement the concepts.

De la lección

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!

Time series examples4:04

Machine learning applied to time series1:55

Common patterns in time series5:05

Introduction to time series4:33

Train, validation and test sets3:21

Metrics for evaluating performance2:20

Moving average and differencing2:45

Trailing versus centered windows1:00

Forecasting4:36

Impartido por:

Laurence Moroney
AI Advocate

Transcripción

Let's take a look at time series and

the various attributes of time series using Python.

This notebook is available as part of

the course and I'll provide a link to it.

I recommend that you watch this video first

and then try the notebook for yourself afterwards.

I'll start by running the nodes containing

the imports as well as a couple of helper functions.

One to plot the series and one to return a trend.

So now, let's plot our first very simple time series.

Even though it's a straight line,

it's also an example of the time series.

The x-axis in this case is time and

the y value is the value of the function at that time.

Next, we'll take a look at adding

a seasonal pattern to our time series.

These functions contain a seasonal pattern and

then seasonality that just uses the same pattern.

We'll now plot that.

As we investigate the graph,

we can see clear peaks and troughs.

But in addition to that,

there are smaller, regular spikes.

This could be seen as a rough simulation

of a seasonal value.

For example, maybe profits for shop that

are negative on the day the store is closed,

peaking a little the day after,

decaying during the week

and then peaking again on the weekend.

What if we now add a trend to this so that the seasonal

data while still following the

pattern increases over time?

Maybe simulating a growing business so when we plot it,

we'll see the same pattern but

with an overall upward trend.

What if we now add another feature that's

common in time series, noise?

Here's a function that add

some noise to a series and when

we call that and plot the results

and their impact on our time series,

we now get a very noisy series,

but one which follows

the same seasonality as we saw earlier.

It's interesting because at this point,

the human eye may miss a lot of

the seasonality data but

a computer will hopefully be able to spot it.

Next we can explore a little bit of Autocorrelation,

but first here are a couple of

functions that can add it for you.

Here is where we add

the autocorrelation to the series and plot it.

There are two different autocorrelation functions and

I'll plot both so that you can see the effects of each.

This one is particularly interesting because you can see

the repeated pattern despite different scales.

There is a pattern and then a sharp fall off followed by

the same pattern on a smaller scale

with the same fall off,

which is then shrunk et cetera.

If I change autocorrelation functions and run it again,

we can then see the other function.

Okay. Let's add some noise.

Then we'll try another autocorrelation and another.

Now, let's add them to simulate

a seasonal time series that

has an impact full of events that changes everything.

For example, that might be a financial series

that shows seasonality,

but then something changes like

a failure of the business or big news events.

Now, I'm going to add some impulses and plot them.

Nothing too exciting here yet.

But when I start adding some autocorrelations to this,

then we'll see some of the behavior that we had

discussed earlier where from

our pulse we have a decay away from it

but the decay could be interrupted by another pulse.

This decay could be autocorrelated so that after

the pulse it decays but then the decay autocorrelates.

So we have these decreasing curves.

Hopefully this exploration of

some synthetic data to show some of

the attributes of time-series was

helpful for you to understand some of the terminology.

I have found that synthetic data

like this is very useful if you

want to learn how to use

Machine Learning to understand and predict on data.

In the next lesson, you'll take

the first steps towards predicting

the next values in

a synthetic series before later in the course,

you'll start applying what you've

learned to real-world data.

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