# 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