Holt-Winters Method (Additive Models) - Part 2

Chapter 3: Lesson 4

Learning Outcomes

Implement the Holt-Winter method to forecast time series
  • Compute the Holt-Winters estimate by hand
  • Use HoltWinters() to forecast additive model time series
  • Plot the Holt-Winters decomposition of a time series (see Fig 3.10)
  • Plot the Holt-Winters fitted values versus the original time series (see Fig 3.11)
  • Superimpose plots of the Holt-Winters predictions with the time series realizations (see Fig 3.13)

Preparation

  • Review Section 3.4.2 (Page 59 - top of page 60 only)

Small Group Activity: Holt-Winters Model for Residential Natural Gas Consumption (35 min)

The United States Energy Information Administration (EIA) publishes data on the total residential natural gas consumption in the country. This government agency publishes monthly data beginning in January 1973. For the purpose of this example, we will only consider quarterly values beginning in 2017. The data are given in MMcf (thousand-thousand cubic feet, or millions of cubic feet). We convert the data to billions of cubic feet (Bcf) and round to the nearest integer to make the numbers a little more manageable.

Show the code 
nat_gas <- rio::import("https://byuistats.github.io/timeseries/data/natural_gas_res.csv") |>
  mutate(date = my(month)) |>
  filter(date >= my("Jan 2017")) |>
  mutate(quarter = yearquarter(date)) |>
  group_by(quarter) |> 
  summarize(
    gas_use_mmcf = sum(residential_nat_gas_consumption),
    n = n()
  ) |>
  filter(n == 3) |>  # Eliminate partial quarter(s)
  dplyr::select(-n) |>
  mutate(gas_billion_cf = round(gas_use_mmcf / 10^3))
quarter gas_use_mmcf gas_billion_cf
2017 Q1 1990316 1990
2017 Q2 602515 603
2017 Q3 325786 326
2017 Q4 1494706 1495
2018 Q1 2330552 2331
2018 Q2 729111 729
⋮ ⋮ ⋮
2022 Q2 709645 710
2022 Q3 327101 327
2022 Q4 1590005 1590
2023 Q1 2114999 2115
2023 Q2 663003 663
2023 Q3 328735 329

The quarters are defined as:

  • Quarter 1: Jan, Feb, Mar
  • Quarter 2: Apr, May, Jun
  • Quarter 3: Jul, Aug, Sep
  • Quarter 4: Oct, Nov, Dec

The weather is colder in the first and fourth quarters, so the demand for natural gas will be higher then. As illustrated in Figure 1, the difference in the consumption in the lowest and highest quarters of a year tends to be about 2000 Bcf.

Figure 1: Quarterly U.S. natural gas consumption (Bcf)

We will use this information to create an initial estimate of the seasonality of the time series. We will assume that in Quarters 1 and 4, natural gas use is 1000 Bcf above the level of the time series and in Quarters 2 and 3, it is 1000 Bcf below the level of the time series. This is not accurate, but it gives us a reasonable starting point.

The portion of the time series we are using begins in the first quarter of 2017. So we will choose the initial values of \(s_t\) as: \[ s_{Q1} = 1000, ~~~ s_{Q2} = -1000, ~~~ s_{Q3} = -1000, ~~~ s_{Q4} = 1000 \]

With \(p=4\) quarters in a year, we implement initial seasonality estimates in the Holt-Winters model as \[ s_{1-p} = s_{(-3)} = 1000, ~~~ s_{2-p} = s_{(-2)} = -1000, ~~~ s_{3-p} = s_{(-1)} = -1000, ~~~ s_{4-p} = s_{0} = 1000 \]

Tables-Handout-Excel

for parameter values reference the “check your understanding” section below this table

Table 1: Holt-Winters filter for the quarterly natural gas consumption in the U.S. in billions of cubic feet
$$Quarter$$ $$t$$ $$x_t$$ $$a_t$$ $$b_t$$ $$s_t$$ $$\hat x_t$$
2016 Q1 -3 — — — 1000 —
2016 Q2 -2 — — — -1000 —
2016 Q3 -1 — — — -1000 —
2016 Q4 0 — — — 1000 —
2017 Q1 1 1990
2017 Q2 2 603
2017 Q3 3 326
2017 Q4 4 1495
2018 Q1 5 2331 1508 -58 805 2313
2018 Q2 6 729 1519 -44 -1012 507
2018 Q3 7 318 1464 -46 -1110 354
2018 Q4 8 1620 1301 -69 693 1994
2019 Q1 9 2451 1315 -52 871 2186
2019 Q2 10 670 1347 -35 -945 402
2019 Q3 11 323 1336 -30 -1091 245
2019 Q4 12 1573 1221 -47 625 1846
2020 Q1 13 2089 1183 -45 878 2061
2020 Q2 14 751 1250 -23 -856 394
2020 Q3 15 354 1271 -14 -1056 215
2020 Q4 16 1481 1177 -30 561 1738
2021 Q1 17 2345 1211 -17 929 2140
2021 Q2 18 690 1264 -3 -800 464
2021 Q3 19 338 1288 2 -1035 253
2021 Q4 20 1344 1189 -18 480 1669
2022 Q1 21 2337 1218 -9 967 2185
2022 Q2 22 710 1269 3 -752 517
2022 Q3 23 327 1290 7 -1021 269
2022 Q4 24 1590 1260 0 450
2023 Q1 25 2115 1238 -4 949
2023 Q2 26 663 1270 3 -723
2023 Q3 27 329 1288 6 -1021
2023 Q4 28 — — —
2024 Q1 29 — — —
2024 Q2 30 — — —
2024 Q3 31 — — —
2024 Q4 32 — — —
2025 Q1 33 — — —
2025 Q2 34 — — —
2025 Q3 35 — — —
2025 Q4 36 — — —
Holt-Winters Update Equations (Additive Model)

Recall the three Holt-Winters update equations for an additive model are:

\[\begin{align*} a_t &= \alpha \left( x_t - s_{t-p} \right) + (1-\alpha) \left( a_{t-1} + b_{t-1} \right) && \text{Level} \\ b_t &= \beta \left( a_t - a_{t-1} \right) + (1-\beta) b_{t-1} && \text{Slope} \\ s_t &= \gamma \left( x_t - a_t \right) + (1-\gamma) s_{t-p} && \text{Seasonal} \end{align*}\]

where \(\{x_t\}\) is a time series from \(t=1\) to \(t=n\) that has seasonality with a period of \(p\) time units; at time \(t\), \(a_t\) is the estimated level of the time series, \(b_t\) is the estimated slope, and \(s_t\) is the estimated seasonal component; and \(\alpha\), \(\beta\), and \(\gamma\) are parameters (all between 0 and 1).

The forecasting equation is: \[ \hat x_{n+k|n} = a_n + k b_n + s_{n+k-p} \]

The details of these computations are given in Chapter 3 Lesson 3.

Check Your Understanding

Apply Holt-Winters filtering to these data. Use \(\alpha = \beta = \gamma = 0.2\).

  • Find \(a_1\)
  • Find \(b_1\)
  • Compute the missing values of \(a_t\), \(b_t\), and \(s_t\) for all quarters from Q1 of 2017 to the end of the data set.
  • Find \(\hat x_t\) for all rows where \(t \ge 1\). Note that the expression to compute \(\hat x_t\) is different for the rows with data versus the rows where forecasting is required.
  • Superimpose a sketch of your Holt-Winters filter and the associated forecast on Figure 1.

Small Group Activity: Application of Holt-Winters in R using the Baltimore Crime Data (20 min)

Background

The City of Baltimore publishes crime data, which can be accessed through a query. This dataset is sourced from the City of Baltimore Open Data. You can explore the data on data.world.

Use the following code to import the data:

Show the code 
crime_df <- rio::import("https://byuistats.github.io/timeseries/data/baltimore_crime.parquet")

The data set consists of 285807 rows and 12 columns. There are a few key variables:

  • Date and Time: Records the date and time of each incident.
  • Location: Detailed coordinates of each incident.
  • Crime Type: Description of the type of crime.

When exploring a new time series, it is crucial to carefully examine the data. Here are a few rows of the original data set. Note that the data are not sorted in time order.

CrimeDate CrimeTime CrimeCode Location Description Inside.Outside Weapon Post District Neighborhood Location.1 Total.Incidents
11/12/2016 02:35:00 3B 300 SAINT PAUL PL ROBBERY - STREET O NA 111 CENTRAL Downtown (39.2924100000, -76.6140800000) 1
11/12/2016 02:56:00 3CF 800 S BROADWAY ROBBERY - COMMERCIAL I FIREARM 213 SOUTHEASTERN Fells Point (39.2824200000, -76.5928800000) 1
11/12/2016 03:00:00 6D 1500 PENTWOOD RD LARCENY FROM AUTO O NA 413 NORTHEASTERN Stonewood-Pentwood-Winston (39.3480500000, -76.5883400000) 1
11/12/2016 03:00:00 6D 6600 MILTON LN LARCENY FROM AUTO O NA 424 NORTHEASTERN Westfield (39.3626300000, -76.5516100000) 1
11/12/2016 03:00:00 6E 300 W BALTIMORE ST LARCENY O NA 111 CENTRAL Downtown (39.2893800000, -76.6197100000) 1
11/12/2016 03:00:00 4E 6900 MCCLEAN BLVD COMMON ASSAULT I HANDS 423 NORTHEASTERN Hamilton Hills (39.3707000000, -76.5670900000) 1
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
01/01/2011 23:00:00 7A 2500 ARUNAH AV AUTO THEFT O NA 721 WESTERN Evergreen Lawn (39.2954200000, -76.6592800000) 1
01/01/2011 23:25:00 4E 100 N MONROE ST COMMON ASSAULT I HANDS 714 WESTERN Penrose/Fayette Street Outreach (39.2899900000, -76.6470700000) 1
01/01/2011 23:38:00 4D 800 N FREMONT AV AGG. ASSAULT I HANDS 123 WESTERN Upton (39.2981200000, -76.6339100000) 1
Check Your Understanding
  • Using the command crime_df |> summary(), we learn that the Total.Incidents always equals 1. What does each row in the data frame represent?

We now summarize the data into a daily tsibble.

Show the code 
# Data Summary and Aggregation
# Group by dates column and summarize from Total.Incidents column
daily_summary_df <- crime_df |>
  rename(dates = CrimeDate) |>
  group_by(dates) |>
  summarise(incidents = sum(Total.Incidents))

# Data Transformation and Formatting
# Select relevant columns, format dates, and arrange the data
crime_data <- daily_summary_df |>
  mutate(dates = mdy(dates)) |>
  mutate(
    month = month(dates),
    day = day(dates),
    year = year(dates)
  ) |>
  arrange(dates) |>
  dplyr::select(dates, month, day, year, incidents)
  
# Convert formatted data to a tsibble with dates as the index
crime_tsibble <- as_tsibble(crime_data, index = dates)

Here are a few rows of the data after summing the crime incidents each day.

dates month day year incidents
2011-01-01 1 1 2011 185
2011-01-02 1 2 2011 102
2011-01-03 1 3 2011 106
2011-01-04 1 4 2011 113
2011-01-05 1 5 2011 131
2011-01-06 1 6 2011 107
⋮ ⋮ ⋮ ⋮ ⋮
2016-11-10 11 10 2016 109
2016-11-11 11 11 2016 115
2016-11-12 11 12 2016 69

Here is a time plot of the number of crimes reported in Baltimore daily.

Show the code 
# Time series plot of total incidents over time
crime_plot <- autoplot(crime_tsibble, .vars = incidents) +
  labs(
    x = "Time",
    y = "Total Crime Incidents",
    title = "Total Crime Incidents Over Time"
  ) +
  theme(plot.title = element_text(hjust = 0.5))

# Display the plot
crime_plot

Check Your Understanding
  • What do you notice about this time plot?
    • Describe the trend
    • Is there evidence of seasonality?
    • Is the additive or multiplicative model appropriate?
    • Which date has the highest number of recorded crimes? Can you determine a reason for this spike?

The following table summarizes the number of days in each month for which crime data were reported.

Show the code 
crime_data |>
  mutate(month_char = format(as.Date(dates), '%b') ) |>
  group_by(month, month_char, year) |>
  summarise(n = n(), .groups = "keep") |>
  group_by() |>
  arrange(year, month) |>
  dplyr::select(-month) |>
  rename(Year = year) |>
  pivot_wider(names_from = month_char, values_from = n) |>
  display_table()
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2011 31 28 31 30 31 30 31 31 30 31 30 31
2012 31 29 31 30 31 30 31 31 30 31 30 31
2013 31 28 31 30 31 30 31 31 30 31 30 31
2014 31 28 31 30 31 30 31 31 30 31 30 31
2015 31 28 31 30 31 30 31 31 30 31 30 31
2016 31 29 31 30 31 30 31 31 30 31 12 NA
Check Your Understanding
  • What do you observe about the data?
  • What are some problems that could arise from incomplete data?
  • How do you recommend we address the missing data?

Monthly Summary

We could analyze the data at the daily level, but for simplicity we will model the monthly totals.

Show the code 
crime_monthly_ts <- crime_tsibble |>
  as_tibble() |>
  mutate(months = yearmonth(dates)) |>
  group_by(months) |>
  summarize(value = sum(incidents)) |>
  as_tsibble(index = months) 

# Plot mean annual total incidents using autoplot
autoplot(crime_monthly_ts, .vars = value) +
  labs(
    x = "Year",
    y = "Total Monthly Crime Incidents",
  ) +
  theme(plot.title = element_text(hjust = 0.5))

There is incomplete data for 2016, as data were not provided after 11/12/2016. We will omit any data after October 2016.

Show the code 
crime_monthly_ts1 <- crime_monthly_ts |>
  filter(months < yearmonth(mdy("11/1/2016")))

We apply Holt-Winters filtering on the monthly Baltimore crimes data with an additive model:

Show the code 
crime_hw <- crime_monthly_ts1 |>
  tsibble::fill_gaps() |>
  model(Additive = ETS(value ~
        trend("A") +
        error("A") +
        season("A"),
        opt_crit = "amse", nmse = 1))
report(crime_hw)
Series: value 
Model: ETS(A,A,A) 
  Smoothing parameters:
    alpha = 0.4102911 
    beta  = 0.0001719 
    gamma = 0.001008159 

  Initial states:
     l[0]      b[0]      s[0]     s[-1]    s[-2]    s[-3]    s[-4]    s[-5]
 4318.875 -4.475258 -83.12823 -38.36167 374.3369 217.8943 416.0997 428.0887
    s[-6]    s[-7]     s[-8]     s[-9]    s[-10]    s[-11]
 301.8743 366.7473 -141.8365 -334.7158 -1039.907 -467.0918

  sigma^2:  45518.72

     AIC     AICc      BIC 
1064.040 1075.810 1102.265

We can compute some values to assess the fit of the model:

Show the code 
# SS of random terms
sum(components(crime_hw)$remainder^2, na.rm = T)

# RMSE
forecast::accuracy(crime_hw)$RMSE

# Standard devation of number of incidents each month
sd(crime_monthly_ts1$value)
  • The sum of the square of the random terms is: 2.4580111^{6}.
  • The root mean square error (RMSE) is: 187.388486.
  • The standard deviation of the number of incidents each month is 22.9761197.

Figure 2 illustrates the Holt-Winters decomposition of the Baltimore crime data.

Show the code 
autoplot(components(crime_hw))
Figure 2: Monthly Total Number of Crime Reported in Baltimore

In Figure 3, we can observe the relationship between the Holt-Winters filter and the time series of the number of crimes each month.

Show the code 
augment(crime_hw) |>
  ggplot(aes(x = months, y = value)) +
    coord_cartesian(ylim = c(0,5500)) +
    geom_line() +
    geom_line(aes(y = .fitted, color = "Fitted")) +
    labs(color = "")
Figure 3: Superimposed plots of the number of crimes each month and the Holt-Winters filter

Figure 4 contains the information from Figure 3, with the addition of an additional four years of forecasted values. The light blue bands give the 95% prediction bands for the forecast.

Show the code 
crime_forecast <- crime_hw |>
  forecast(h = "4 years") 
crime_forecast |>
  autoplot(crime_monthly_ts1, level = 95) +
  coord_cartesian(ylim = c(0,5500)) +
  geom_line(aes(y = .fitted, color = "Fitted"),
    data = augment(crime_hw)) +
  scale_color_discrete(name = "")
Figure 4: Superimposed plots of the number of crimes each month and the Holt-Winters filter, with four additional years forecasted

Rethinking Baltimore

The monthly crime data shows a distinct pattern arcing through the annual cycle. Consider the data for 2011.

Table 2: Total count of crimes reported in Baltimore in 2011 by month
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3440 3108 4269 4149 4516 4427 4669 4574 4377 4644 4411 4067
Check Your Understanding
  • Starting with January, determine whether the number of crimes goes up or down as you move from one month to the next.
  • What might explain this pattern?
  • Use the function days_in_month() to adjust the time series and re-run the analysis. What do you notice?

Homework Preview (5 min)

  • Review upcoming homework assignment
  • Clarify questions
Download Homework

homework_3_4.qmd

Natural Gas Holt-Winters Filter

Tables-Handout-Excel-key

Balitmore Crime Spike

Balitmore (Mean Crimes Per Day)

References