1. Timeseries kinds and applications
Welcome to Introduction to Machine Learning for Timeseries Data. This course is focused on the intersection of Machine Learning and Time series data, and hence we expect you have taken introductory courses on Machine learning and time series analysis here on DataCamp.
2. Time Series
This course focuses on machine learning in the context of timeseries data. Put simply, a timeseries means data that changes over time. This can take many different forms, such as atmospheric CO2 over time, the waveform of my voice as I am speaking.
3. Time Series
the fluctuation of a stock's value over the year, or demographic information about a city.
4. What makes a time series?
Timeseries data consists of at least two things: One, an array of numbers that represents the data itself. Two, another array that contains a timestamp for each datapoint. The timestamps can include a wide range of time data, from months of the year to nanoseconds.
5. Reading in a time series with Pandas
Here we import timeseries data into a pandas DataFrame. Note that each datapoint has a corresponding time point (in this case, a date), though multiple datapoints may have the same time point.
6. Plotting a pandas timeseries
Here is the code to plot this timeseries data with Matplotlib
and Pandas. We first create a figure and axis, then read in the data
with Pandas and use the dot-plot method to plot the data on the axis.
7. A timeseries plot
The amount of time that passes between timestamps defines the "period" of the timeseries. In this case, it is about one day. This often helps us infer what kind of timeseries we're dealing with.
8. Why machine learning?
Machine learning has taken the world of data science by storm. In the last few
decades, advances in computing power, algorithms, and community practices have
made it possible to use computers to ask questions that were never thought possible.
Machine learning is about finding patterns in data - often patterns that are not
immediately obvious to the human eye. This is often because the data is either too large or too complex to be processed by a human.
9. Why machine learning?
Another crucial part of machine learning is that we can build a model of the world that formalizes our knowledge of the problem at hand. We can use this model to make predictions. Combined with automation, this can be a critical component of an organization's decision making.
10. Why combine these two?
Why should we treat timeseries any differently from another data set? Well,
machine learning is all about finding patterns in data. Timeseries data always change over time, which turns out to be a useful pattern to utilize. For example, here is a raw waveform of someone speaking, and here is a collection of timeseries features that were extracted from it. As you can see, using timeseries-specific features lets us see a much richer representation of the raw data.
11. A machine learning pipeline
This course will focus on a simple machine learning pipeline in the context of timeseries data. This boils down to the following main steps. Feature extraction: what kinds of special features leverage a signal that changes over time? Model fitting: what kinds of models are suitable for asking questions with timeseries data? Validation: How can we validate a model that uses timeseries data? What considerations must we make because it changes in time?
12. Let's practice!
Let's start by plotting some time series data.