Event Forecasting for Thailand’s Car Sales during the COVID-19 Pandemic
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. The Typical Holt’s Method
- Step 1: Computing the Level Estimate.
- Step 2: Computing the Trend Estimate.
- Step 3: Computing the Holt’s Estimate.
3.2. The Holt’s Method with Seasonality
- Step 1 (Additional): Dealing with Seasonality.
- Step 1.1: Finding the Moving Averages (MAs).
- Step 1.2: Finding the Centered Moving Averages (CMAs).
- Step 1.3: Computing the Seasonal Factors.
- Step 1.4: Computing the Unscaled Seasonal Indices.
- Step 1.5: Computing the (Scaled) Seasonal Indices.
- Step 1.6: Removing Seasonality From the Data (De-seasonalization).
- Steps 2 to 4: Computing Level, Trend, and Holt’s Estimates.
- Step 5 (Additional): Computing the Holt’s Estimate with Seasonality (Re-seasonalization).
3.3. The Holt’s Method with Events
- Steps 1 to 2: Computing Level and Trend Estimates.
- Step 3 (Additional): Computing the Event Estimate.
Ekt = δ (At/Lt) + (1 − δ) Ekt−; k = 1, 2, 3,
- Case 1: when t > 1, the initial value is Ek1 = 1.
- Case 2: when t ≠ 1, the initial value is Ekt = Et−1, where Et−1 refers to the event factor immediately preceding the current period t.
- Step 4: Computing the Holt’s Estimate with Events.
3.4. The Holt’s Method with Seasonality and Events
- Step 1: Dealing with Seasonality.
- Steps 1.1 to 1.3: Computing MAs, CMAs, and the Seasonal Factors.
- Step 1.4: Computing the Unscaled Seasonal Indices of the Normal Sales Periods Only.
- Step 1.5: Computing the (Scaled) Seasonal Indices of the Normal Sales Periods Only.
- Step 1.6: De-seasonalization.
- Steps 2 to 3: Computing Level and Trend Estimates.
- Steps 4 to 5: Computing the Event Estimate and Holt’s Estimate with Events.
- Step 6: Computing the Holt’s Estimate with Events and Seasonality (Re-seasonalization).
3.5. The Accuracy Measurement
4. Results and Discussion
4.1. Implementation of the Holt’s Method with Seasonality and Events on the Car Sales Data
4.2. Numerical Results
4.3. Forecasting Accuracy Comparison and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Period | Yr | Mo | k | Car Sales (‘000 Baht) | MA | CMA | SF | SI Unscaled | SI | Deseason |
---|---|---|---|---|---|---|---|---|---|---|
1 | 2015 | 1 | 0 | 26,977,961.53 | 0.9296 | 0.9178 | 29,395,602.24 | |||
2 | 2015 | 2 | 0 | 27,902,176.73 | 1.0643 | 1.0508 | 26,554,084.21 | |||
3 | 2015 | 3 | 0 | 29,774,248.84 | 1.0950 | 1.0811 | 27,541,580.65 | |||
4 | 2015 | 4 | 0 | 20,635,767.24 | 0.8765 | 0.8653 | 23,847,031.31 | |||
5 | 2015 | 5 | 0 | 26,625,638.70 | 1.0466 | 1.0333 | 25,768,470.42 | |||
6 | 2015 | 6 | 0 | 22,234,255.04 | 24,564,721.20 | 1.1011 | 1.0870 | 20,453,895.19 | ||
7 | 2015 | 7 | 0 | 24,605,387.66 | 23,967,856.58 | 24,266,288.89 | 1.0140 | 0.9903 | 0.9777 | 25,167,107.50 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
61 | 2020 | 1 | 0 | 26,502,129.77 | 23,917,024.70 | 24,333,568.57 | 1.0891 | 0.9296 | 0.9178 | 28,877,128.64 |
62 | 2020 | 2 | 0 | 32,657,258.86 | 23,595,908.11 | 23,756,466.40 | 1.3747 | 1.0643 | 1.0508 | 31,079,424.73 |
63 | 2020 | 3 | 0 | 26,042,182.41 | 23,481,330.89 | 23,538,619.50 | 1.1064 | 1.0950 | 1.0811 | 24,089,369.01 |
64 | 2020 | 4 | 1 | 6,960,649.53 | 23,962,827.37 | 23,722,079.13 | 0.2934 | 0.8765 | 0.8653 | 8,043,840.84 |
65 | 2020 | 5 | 1 | 9,204,254.83 | 24,722,380.43 | 24,342,603.90 | 0.3781 | 1.0466 | 1.0333 | 8,907,939.10 |
66 | 2020 | 6 | 1 | 14,674,115.15 | 25,217,869.42 | 24,970,124.93 | 0.5877 | 1.1011 | 1.0870 | 13,499,117.14 |
67 | 2020 | 7 | 1 | 21,454,176.54 | 25,070,934.52 | 25,144,401.97 | 0.8532 | 0.9903 | 0.9777 | 21,943,956.93 |
68 | 2020 | 8 | 2 | 28,104,687.94 | 24,771,931.36 | 24,921,432.94 | 1.1277 | 1.0040 | 0.9912 | 28,353,320.27 |
69 | 2020 | 9 | 2 | 31,278,826.07 | 25,133,393.68 | 24,952,662.52 | 1.2535 | 1.0758 | 1.0621 | 29,448,765.08 |
70 | 2020 | 10 | 2 | 33,105,584.68 | 26,234,010.91 | 25,683,702.29 | 1.2890 | 0.9447 | 0.9327 | 35,495,335.99 |
71 | 2020 | 11 | 2 | 37,805,573.95 | 26,902,365.10 | 26,568,188.00 | 1.4230 | 1.0143 | 1.0014 | 37,754,524.32 |
72 | 2020 | 12 | 2 | 34,824,993.37 | 27,635,278.36 | 27,268,821.73 | 1.2771 | 1.0126 | 0.9997 | 34,835,988.33 |
73 | 2021 | 1 | 3 | 24,738,910.91 | 27,648,750.56 | 27,642,014.46 | 0.8950 | 0.9296 | 0.9178 | 26,955,898.22 |
74 | 2021 | 2 | 2 | 29,069,220.98 | 26,938,095.62 | 27,293,423.09 | 1.0651 | 1.0643 | 1.0508 | 27,664,742.76 |
75 | 2021 | 3 | 2 | 30,379,730.20 | 26,384,499.74 | 26,661,297.68 | 1.1395 | 1.0950 | 1.0811 | 28,101,659.06 |
76 | 2021 | 4 | 3 | 20,168,056.27 | 25,893,933.88 | 26,139,216.81 | 0.7716 | 0.8765 | 0.8653 | 23,306,536.84 |
77 | 2021 | 5 | 3 | 17,224,505.15 | 25,237,711.02 | 25,565,822.45 | 0.6737 | 1.0466 | 1.0333 | 16,669,990.77 |
78 | 2021 | 6 | 3 | 23,469,074.27 | 25,047,140.56 | 25,142,425.79 | 0.9334 | 1.1011 | 1.0870 | 21,589,838.94 |
79 | 2021 | 7 | 3 | 21,615,842.91 | 0.9903 | 0.9777 | 22,109,314.01 | |||
80 | 2021 | 8 | 3 | 19,576,828.62 | 1.0040 | 0.9912 | 19,750,017.97 | |||
81 | 2021 | 9 | 3 | 24,635,675.60 | 1.0758 | 1.0621 | 23,194,291.94 | |||
82 | 2021 | 10 | 2 | 27,218,794.28 | 0.9447 | 0.9327 | 29,183,603.23 | |||
83 | 2021 | 11 | 2 | 29,930,899.65 | 1.0143 | 1.0014 | 29,890,483.35 | |||
84 | 2021 | 12 | 2 | 32,538,147.92 | 1.0126 | 0.9997 | 32,548,420.87 |
Period | Yr | Mo | k | Deseason | L | T | Et- | E | HE | FE |
---|---|---|---|---|---|---|---|---|---|---|
1 | 2015 | 1 | 0 | 29,395,602.24 | 1.0000 | |||||
2 | 2015 | 2 | 0 | 26,554,084.21 | 26,554,084.21 | −2,841,518.04 | 1.0000 | 1.0000 | ||
3 | 2015 | 3 | 0 | 27,541,580.65 | 24,585,859.63 | −2,418,254.32 | 1.0000 | 1.0000 | 23,712,566.17 | 25,634,833.91 |
4 | 2015 | 4 | 0 | 23,847,031.31 | 22,550,636.44 | −2,232,608.62 | 1.0000 | 1.0000 | 22,167,605.31 | 19,182,494.35 |
5 | 2015 | 5 | 0 | 25,768,470.42 | 21,561,124.69 | −1,630,110.35 | 1.0000 | 1.0000 | 20,318,027.83 | 20,993,891.35 |
6 | 2015 | 6 | 0 | 20,453,895.19 | 20,050,269.16 | −1,572,310.49 | 1.0000 | 1.0000 | 19,931,014.34 | 21,665,861.30 |
7 | 2015 | 7 | 0 | 25,167,107.50 | 20,003,570.54 | −832,884.20 | 1.0000 | 1.0000 | 18,477,958.67 | 18,065,537.97 |
⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
61 | 2020 | 1 | 0 | 28,877,128.64 | 27,968,469.87 | −928,188.07 | 1.0000 | 1.0000 | 27,699,998.74 | 25,421,812.91 |
62 | 2020 | 2 | 0 | 31,079,424.73 | 27,961,499.82 | −481,696.51 | 1.0000 | 1.0000 | 27,040,281.80 | 28,413,057.51 |
63 | 2020 | 3 | 0 | 24,089,369.01 | 26,706,537.99 | −856,479.06 | 1.0000 | 1.0000 | 27,479,803.31 | 29,707,463.49 |
64 | 2020 | 4 | 1 | 8,043,840.84 | 21,788,947.66 | −2,824,799.13 | 1.0000 | 0.3692 | 9,543,084.10 | 8,258,003.25 |
65 | 2020 | 5 | 1 | 8,907,939.10 | 16,670,602.24 | −3,936,424.22 | 0.3692 | 0.5344 | 10,133,495.95 | 10,470,578.87 |
66 | 2020 | 6 | 1 | 13,499,117.14 | 12,908,639.70 | −3,851,866.96 | 0.5344 | 1.0457 | 13,316,675.08 | 14,475,792.87 |
67 | 2020 | 7 | 1 | 21,943,956.93 | 11,995,986.94 | −2,427,302.62 | 1.0457 | 1.8293 | 16,567,326.39 | 16,197,550.25 |
68 | 2020 | 8 | 2 | 28,353,320.27 | 13,852,945.94 | −350,827.11 | 1.8293 | 2.0467 | 19,584,568.67 | 19,412,830.15 |
69 | 2020 | 9 | 2 | 29,448,765.08 | 17,139,112.64 | 1,411,933.73 | 2.0467 | 1.7182 | 23,199,609.81 | 24,641,323.94 |
70 | 2020 | 10 | 2 | 35,495,335.99 | 22,415,575.33 | 3,284,975.22 | 1.7182 | 1.5835 | 29,375,807.40 | 27,398,058.15 |
71 | 2020 | 11 | 2 | 37,754,524.32 | 28,449,732.24 | 4,617,435.51 | 1.5835 | 1.3271 | 34,106,193.08 | 34,152,309.63 |
72 | 2020 | 12 | 2 | 34,835,988.33 | 33,470,587.34 | 4,812,962.99 | 1.3271 | 1.0408 | 34,416,111.62 | 34,405,249.19 |
73 | 2021 | 1 | 3 | 26,955,898.22 | 35,700,022.74 | 3,560,791.15 | 1.0408 | 0.7551 | 28,906,633.86 | 26,529,208.34 |
74 | 2021 | 2 | 2 | 27,664,742.76 | 36,616,067.26 | 2,278,947.95 | 1.0408 | 0.7555 | 29,662,943.02 | 31,168,865.47 |
75 | 2021 | 3 | 2 | 28,101,659.06 | 36,433,345.92 | 1,085,837.80 | 0.7555 | 0.7713 | 30,000,386.43 | 32,432,378.60 |
76 | 2021 | 4 | 3 | 23,306,536.84 | 34,277,667.74 | −485,244.72 | 0.7551 | 0.6799 | 25,510,552.36 | 22,075,276.95 |
77 | 2021 | 5 | 3 | 16,669,990.77 | 29,887,264.59 | −2,377,978.31 | 0.6799 | 0.5578 | 18,848,141.09 | 19,475,109.96 |
78 | 2021 | 6 | 3 | 21,589,838.94 | 26,159,222.26 | −3,032,320.91 | 0.5578 | 0.8253 | 22,704,079.43 | 24,680,301.13 |
79 | 2021 | 7 | 3 | 22,109,314.01 | 22,894,817.52 | −3,144,806.20 | 0.8253 | 0.9657 | 22,333,435.23 | 21,834,961.84 |
80 | 2021 | 8 | 3 | 19,750,017.97 | 19,750,012.84 | −3,144,805.46 | 0.9657 | 1.0000 | 19,750,016.46 | 19,576,827.12 |
81 | 2021 | 9 | 3 | 23,194,291.94 | 18,107,997.33 | −2,416,440.39 | 1.0000 | 1.2809 | 21,269,388.36 | 22,591,151.02 |
82 | 2021 | 10 | 2 | 29,183,603.23 | 18,768,723.64 | −925,013.89 | 0.7713 | 1.5549 | 24,398,897.91 | 22,756,222.99 |
83 | 2021 | 11 | 2 | 29,890,483.35 | 20,591,249.28 | 406,650.48 | 1.5549 | 1.4516 | 25,902,124.83 | 25,937,148.28 |
84 | 2021 | 12 | 2 | 32,548,420.87 | 23,632,257.67 | 1,683,458.53 | 1.4516 | 1.3773 | 28,920,151.78 | 28,911,023.99 |
Mo | SI Unscaled | SI |
---|---|---|
1 | 0.9296 | 0.9178 |
2 | 1.0643 | 1.0508 |
3 | 1.0950 | 1.0811 |
4 | 0.8765 | 0.8653 |
5 | 1.0466 | 1.0333 |
6 | 1.1011 | 1.0870 |
7 | 0.9903 | 0.9777 |
8 | 1.0040 | 0.9912 |
9 | 1.0758 | 1.0621 |
10 | 0.9447 | 0.9327 |
11 | 1.0143 | 1.0014 |
12 | 1.0126 | 0.9997 |
Sum | 12.1547 | 12.0000 |
Method | MAPE | SMAPE |
---|---|---|
Holt | 16.27% | 13.91% |
Holt S | 12.37% | 11.99% |
Holt E | 9.47% | 9.33% |
Holt SE | 8.64% | 8.90% |
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Leenawong, C.; Chaikajonwat, T. Event Forecasting for Thailand’s Car Sales during the COVID-19 Pandemic. Data 2022, 7, 86. https://doi.org/10.3390/data7070086
Leenawong C, Chaikajonwat T. Event Forecasting for Thailand’s Car Sales during the COVID-19 Pandemic. Data. 2022; 7(7):86. https://doi.org/10.3390/data7070086
Chicago/Turabian StyleLeenawong, Chartchai, and Thanrada Chaikajonwat. 2022. "Event Forecasting for Thailand’s Car Sales during the COVID-19 Pandemic" Data 7, no. 7: 86. https://doi.org/10.3390/data7070086