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.
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