Python for Time Series Analysis and Forecasting
- 5 hours on-demand video
- 2 articles
- 6 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- use Python to perform calculations with time and date based data
- create models for time series data
- use models for forecasting
- identify which models are suitable for a given dataset
- visualize time series data
- create ARIMA and exponential smoothing models
- know how to interpret given models
- understand time series statistics such as autocorrelation or stationarity
- use machine learning and deep learning for time series
- know the alternatives to qualitative methods
- know how to read a time series plot and understand it (trend, seasonality, constant mean and variance)
- computer with Python and Anaconda ready to use
- basic Python and Anaconda knowledge (installing packages, Anaconda usage, Python basics)
- no statistics or time series knowledge is required before the course, you will learn all the relevant things!
- time and patience to reproduce the examples and to solve the exercises
Use Python to Understand the Now and Predict the Future!
Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to
see patterns in time series data
model this data
finally make forecasts based on those models
and of of this you can now do with the help of Python
Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data collected in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!
What will you learn in this course and how is it structured?
First of all we will discuss the general idea behind time series analysis and forecasting. It is important to know when to use these tools and what they actually do.
After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests. You will also learn how to read a time series chart. This is a crucial skill because things like mean, variance, trend or seasonality are a determining factor for model selection.
We will also create our own time series charts including smoothers and trend lines.
Then you will see how different models work, how they are set up in Python and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied by homework assignments.
Where are those methods applied?
In nearly any field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.
Is it hard to understand and learn those methods?
Unfortunately learning material on Time Series Analysis Programming in Python is quite technical and needs tons of prior knowledge to be understood.
With this course it is the goal to make modeling and forecasting as intuitive and simple as possible for you.
While you need some knowledge in maths and Python, the course is meant for people without a major in a quantitative field. Basically anybody dealing with time data on a regular basis can benefit from this course.
How do I prepare best to benefit from this course?
It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in Python.
- data analysts working with time series data (which is essentially any data analyst at some point in the career)
- people using Python
- this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science
- generally if you have time series data on your table and you do not know what to do with it and Python, take this course!