DS 786 – Week 3

  • Watch Videos
    • Forecasting- Linear Trend
      • Let’s consider more patterns
      • Smoothing filters work best for stable timeseries data when there aren’t trends or seasonality
      • Seasonality (like sine waves) is its own thing with different analytical techniques
      • Trends up and down are not good for smoothing filters; regressions are
      • Nonlinear trend analysis
        • Not what we’re doing in this class
      • Linear trend analysis
        • y=mx+b
        • Finding the intercept and slope lets us make predictions about the future
        • All forecasts work better in the short-term than the long-term
    • Associative Forecasting
      • Maybe other variables are better for regressions than time.
      • An associative forecast compares variables rather than using time as a variable.
      • Linear regression
        • Linear trend analysis
          • y = intercept + slope * time
        • Associative forecast
          • y = intercept + slope * value of second variable at each time
    • What makes regressions accurate
      • r^2 is the percentage of variation in y that is explained by x
      • correlation (r)
        • Correlations can be positive and negative
          • Positive: trend together
          • Negative: trend in opposite directions
        • Correlations can be spurious
        • Correlation is signed, where r^2 gives an absolute value.
  • Quiz