Time Series Analysis in Python 2019

Time Series Analysis in Python 2019, Time Series Analysis in Python: Econometric Theory, Financial Modeling (ARMA, Integrated, MAX and Volatility Models)

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What you'll learn

• Differentiate between time series data and cross-sectional data.
• Understand the fundamental assumptions of time series data and how to take advantage of them.
• Transforming a data set into a time-series.
• Start coding in Python and learn how to use it for statistical analysis.
• Carry out time-series analysis in Python and interpreting the results, based on the data in question.
• Examine the crucial differences between related series like prices and returns.
• Comprehend the need to normalize data when comparing different time series.
• Encounter special types of time series like White Noise and Random Walks.
• Learn about "autocorrelation" and how to account for it.
• Learn about accounting for "unexpected shocks" via moving averages.
• Discuss model selection in time series and the role residuals play in it.
• Comprehend stationarity and how to test for its existence.
• Acknowledge the notion of integration and understand when, why and how to properly use it.
• Realize the importance of volatility and how we can measure it.
• Forecast the future based on patterns observed in the past.