Time Series Analysis in Python. Master Applied Data Analysis
Python Time Series Analysis with 10+ Forecasting Models including ARIMA, SARIMA, Regression & Time Series Data Analysis
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.
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