Ratings & reviews

3.01 rating

Ghada S.

11 days ago

Not as expected! kind of challenging to be an intermediate level course. Has few bugs, which was pretty annoying!

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Complete all chapters to get certificate

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Time Series: Let's Start

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Start from the introduction to time series to move on to more in-depth topics. Find out what it is, how often time series occur in real business, how to analyze them and make forecasts.

Introduction

Practical Examples

Model Types

Implementation in Python

Visualization

Time Series Processing

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What is the difference between trend and seasonality? Is the data I'm working with stationary or not? In this section, you will learn how to analyze characteristics of time series using Python!

Reading and Visualizing Data

Stationarity

Autocorrelation

Decomposition

Time Series Visualization

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Learn more about time series through… their visualization! Explore your data with 2D and 3D graphs.

Simple Time Series

Customize Your Plot

Challenge

Stationary Models

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What will be our first step? Let's start with stationary models! Create your first predictive model that will forecast time series.

Examples of Stationary Time Series

Simple Moving Average

Autoregression

Autoregressive-Integrated-Moving-Average Model

Challenge

Non-Stationary Models

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Most of the data you work with is non-stationary. Learn how to process such data and what models to use to predict it.

Examples of Non-Stationary Time Series

Convert Non-Stationary Data to Stationary

Challenge 1

Challenge 2

Solve Real Problems

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Let's dive into a variety of interesting tasks that you may encounter while working with time series! Find out how big these challenges are and what models we can use to solve them.

Weather Forecasting

Store Demand Forecast

Financial Markets

Predict the Emergence of Repeat Customers

Medicine: EEG Forecasting