Time Series Analysis in Python 2019

Time Series Analysis in Python 2019

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.

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