Time Series Analysis in Python. Master Applied Data Analysis Course
Python Time Series Analysis with 10+ Forecasting Models including ARIMA, SARIMA, Regression & Time Series Data Analysis
The Ultimate course on Time Series Analysis in Python which brings you expertise in Forecasting Models, Regression, ARIMA, SARIMA and Time Series Data Analysis with Python. Time Series Analysis has tons of applications such as stock market analysis, pattern recognition, earthquake prediction, census analysis and many more. This course begins with the basic level and goes up to the most advanced techniques step by step. Even if you do not know anything about time series, this course will make complete sense to you. This course is for everyone who wants to master time series and become proficient in working with real life time based data. For taking up this course you need to have prior knowledge of Python programming.
What you’ll learn
- What is Time Series Data, it applications and components.
- Fetching time series data using different methods.
- Handling missing values and outliers in a time series data.
- Decomposing and Splitting time series data.
- Different smoothing techniques such as Simple Moving Averages, Simple Exponential, Holt and Holt-winter Exponential.
- Checking Stationarity of the time series data and Converting Non-stationary to Stationary.
- Auto-regressive models such as Simple AR model and Moving Average Model.
- Advanced Auto-Regressive Models such as ARMA, ARIMA, SARIMA.
- Evaluation Metrics used for time series data.
- Rules for Choosing the Right Model for time series data.
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