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Genre: eLearning
What you’ll learn
What is Time Series Data, it’s 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.
Who this course is for:
Programming Beginners
Data Science Enthusiast
Python Developers
Programmers who wants to specialize in finance
Machine Learning Enthusiast
Beginner Python Developers curious to Learn about Time Series
Statisticians who want to Upgrade themselves
Data Analysts who want to Upgrade themselves
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