We have 2025 data for annual salaries of 75 Upstate NY residents ranging from $50K to $150K and we use that data to model how much someone spends on their first house.
Is it valid to apply that model to someone with an annual salary of $350K?
Was mostly trending upward, but had a downturn and then another recent upturn.
Data shown are daily adjusted closing value
For analysis, we will use are monthly adjusted close (1st day of trading for each month)
Netflix Stock
Forecast Questions:
What will be the estimated stock price be in April of 2027?
What ARIMA model was chosen (p,d,q)?
Model Assessment Questions:
How valid is our model?
Check residual plots.
How are accurate are our estimates?
Examine Prediction Intervals and Prediction Bands
Check fit statistics
Netflix Stock - Modeling Time Series Data
Stock Trend Forecast
Create time series using Netflix Stock data
Specify freq = 12 - 12 observations per year
Specify start = c(2010, 1) - first obs. in dataset is January 2010
Model data using auto.arima function
Specify ic = aic - aic is the information criterion used to determine model.
Specify seasonality = F - no seasonal (repeating) pattern in the data.
This code will create and save the model:
nflx_ts <-ts(nflx$Adjusted, freq=12, start=c(2010,1)) # create time seriesnflx_model <-auto.arima(nflx_ts, ic="aic", seasonal=F) # model data using auto.arima
Netflix Stock - Create and Plot Forecasts
Create forecasts (until April 2027)
h = 12 indicates we want to forecast 12 months
Most recent date in forecast data is April 1, 2026
12 Months until April 1, 2027
Forecasts become less accurate the further into the future you specify.
In February of 2027, the Netflix stock price is forecasted to be approximately $102. However the 95% prediction interval indicates it may be as low as ____.
Round your answer to the closest whole dollar.
Netflix Stock - Examine Residuals and Model Fit
Top Plot: Spikes get larger over time
ACF: auto-correlation function.
Ideally, all or most values are with dashed lines
Histogram: Distribution of residuals should be approx. normal
Assessment: Stock prices are very volatile and this is sufficient.
Ljung-Box test
data: Residuals from ARIMA(2,1,2) with drift
Q* = 29.59, df = 20, p-value = 0.07677
Model df: 4. Total lags used: 24
Netflix Stock - Examine Residuals and Model Fit
ME RMSE MAE MPE MAPE MASE
Training set 0.0007043361 4.159505 2.555861 -6.764627 13.76645 0.2189833
ACF1
Training set 0.0568807
Many options for comparing models
For BUA 345: We will use MAPE = Mean Absolute Percent Error
100 – MAPE = Percent accuracy of model.
Despite increasing volatility, our stock price model is estimated to be ____% accurate. (Next question)
This doesn’t guarantee that forecasts will be this accurate but it does improve our chances of accurate forecasting.
