Time Series Analysis in Python. Master Applied Data Analysis

Time Series Analysis in Python. Master Applied Data Analysis

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.