| --- |
| license: cc-by-4.0 |
| library_name: prophet |
| pipeline_tag: time-series-forecasting |
| datasets: |
| - thanatchon36/lockdowns-as-partial-holidays |
| language: |
| - en |
| tags: |
| - time-series-forecasting |
| - forecasting |
| - prophet |
| - lightgbm |
| - sarimax |
| - statsmodels |
| - thailand |
| - exports |
| - covid-19 |
| - holidays |
| - calendar-effects |
| - simulated |
| --- |
| |
| # Lockdowns as Partial Holidays: the fitted HES models |
|
|
| > **Trained on simulated exports.** These models were fitted to the simulated daily export series of replication 0 |
| > of the paper's Monte Carlo study (scenario S1, 2010-2019). They reproduce the paper's shock experiment and show |
| > the method at work; they are **not** a forecaster of official Thai exports. To apply the method to official data, |
| > run `hesi real` from the [code repository](https://github.com/thanatchon36/lockdowns-as-partial-holidays), |
| > which refits every model on your series. |
|
|
| Models for the preprint *Lockdowns as Partial Holidays: Identifying Work-Stoppage Elasticities from Recurring |
| Holidays and Transferring Them to Forecast Thailand's Monthly Exports through the COVID-19 Shock* by Thanatchon |
| Chongmankhong ([read online](https://thanatchon36.github.io/lockdowns-as-partial-holidays/paper/) · |
| [PDF](https://github.com/thanatchon36/lockdowns-as-partial-holidays/blob/main/paper/paper.pdf) · |
| [ฉบับภาษาไทย](https://github.com/thanatchon36/lockdowns-as-partial-holidays/blob/main/paper/thai/paper_th.pdf)). |
| Data: [`thanatchon36/lockdowns-as-partial-holidays`](https://huggingface.co/datasets/thanatchon36/lockdowns-as-partial-holidays). |
| Archived on Zenodo: [10.5281/zenodo.24036954](https://doi.org/10.5281/zenodo.24036954). |
|
|
| *Independent personal research. Does not represent the views of any employer or institution.* |
|
|
| ## The idea |
|
|
| A lockdown is treated as a partial holiday. Each model carries one shared stoppage regressor per channel: |
|
|
| ``` |
| x^D_d = H_d + (1 - H_d) * dose_d domestic: H_d = 1 on Thai public holidays (Songkran included) |
| x^P_d = C_d + (1 - C_d) * dose_d partner: C_d = 1 in the Chinese New Year window |
| ``` |
|
|
| Before 2020 every dose is zero, so the elasticities `beta_D`, `beta_P` are learned from ten years of holidays |
| alone. To forecast a lockdown, only the dose changes; no new parameter is estimated. The dose of the paper is the |
| Holiday-Equivalent Stoppage Index (HESI): the drop in workplace mobility on a day divided by its median drop on a |
| weekday public holiday. |
|
|
| ## Files |
|
|
| | file | model | |
| |---|---| |
| | `prophet_daily_he.json` | **Prophet-D HE**: daily Prophet on log exports with regressors `xD`, `xP` (prophet.serialize JSON) | |
| | `prophet_monthly_he.json` | **Prophet-M HE**: monthly Prophet on log average daily exports with `MxD`, `MxP` (monthly means of the daily regressors) and `WK` (weekend share). **Prophet-M HE+AC** is the same model with the closed-form aggregation correction: daily-equivalent elasticities in `meta.json` | |
| | `sarimax_he.json` | **SARIMAX HE**: airline model (0,1,1)(0,1,1)12 on log average daily exports with `MxD`, `MxP`, `WK`; parameters and the 120 training months, rebuilt with statsmodels `SARIMAX(...).filter(params)` | |
| | `lightgbm_daily_he.txt`, `lightgbm_monthly_he.txt` | **LightGBM-D HE**, **LightGBM-M HE**: linear log-trend plus LightGBM on the detrended residual (native text models); trend coefficients in `meta.json` | |
| | `meta.json` | regressors and coefficients (log units), Duan smearing factors, trend coefficients, the aggregation-corrected elasticities, training window, seed, library versions | |
| | `hes_forecast.py` | standalone loader and forecaster (numpy, pandas, prophet, lightgbm, statsmodels; no pickle) | |
| | `forecast_check.csv` | 2020-2021 forecasts of every model and dose variant, the paper's forecasts for the same replication, and the simulated actuals | |
| | `SHA256SUMS` | checksums of every file above | |
|
|
| Nothing is pickled: Prophet loads from JSON, LightGBM from its text format and SARIMAX from its parameters. |
|
|
| ## Elasticities (replication 0) |
|
|
| The true daily elasticities of the simulation are `beta_D = log(0.45) = -0.799` and `beta_P = log(0.80) = -0.223`. |
|
|
| | model | `beta_D` | `beta_P` | |
| |---|---:|---:| |
| | Prophet-D HE (daily) | -0.800 | -0.217 | |
| | Prophet-M HE (monthly, attenuated) | -0.613 | -0.219 | |
| | Prophet-M HE+AC (aggregation-corrected) | -0.880 | -0.240 | |
| | SARIMAX HE (monthly) | -0.752 | -0.256 | |
|
|
| Monthly data attenuate the daily elasticity towards `exp(beta) - 1` (Jensen); the paper derives the attenuation and |
| the correction. Over the 30 replications the means are -0.80 (daily), -0.54 (monthly) and -0.76 (corrected). |
|
|
| ## Use |
|
|
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download, snapshot_download |
| |
| d = snapshot_download("thanatchon36/lockdowns-as-partial-holidays") |
| import sys; sys.path.insert(0, d) |
| import hes_forecast as hf |
| |
| models = hf.load(d) |
| reg = pd.read_csv(hf_hub_download("thanatchon36/lockdowns-as-partial-holidays", "data/regressors_daily.csv", |
| repo_type="dataset"), parse_dates=["date"]) |
| X = reg[reg.variant == "hesi"].set_index("date")[["xD", "xP"]] # the HESI dose |
| months = pd.date_range("2020-01-01", "2021-12-01", freq="MS") |
| fc = hf.forecast(models, X, months) # one column per model |
| ``` |
|
|
| Other doses: `variant` `off` (no dose), `binary` (whole-month switch), `stringency` (raw OxCGRT), `oracle_S1`, |
| `oracle_S2`, or any daily `xD`, `xP` you build yourself (in 0..1, equal to 1 on holidays). |
|
|
| ## How well they forecast the shock (replication 0) |
|
|
| MAPE of monthly exports, January 2020 - December 2021, scenario S1, forecasts made at the end of 2019: |
|
|
| | model | no dose | binary switch | raw stringency | HESI dose | |
| |---|---:|---:|---:|---:| |
| | Prophet-D HE | 30.3% | 26.3% | 24.9% | **8.5%** | |
| | Prophet-M HE | 29.5% | 24.0% | 17.9% | 11.2% | |
| | Prophet-M HE+AC | 29.6% | 26.3% | 29.1% | **7.4%** | |
| | SARIMAX HE | 26.4% | 22.1% | 26.4% | **7.1%** | |
| | LightGBM-D HE | 16.3% | 15.3% | 43.8% | 42.7% | |
| | LightGBM-M HE | 14.9% | 14.2% | 13.6% | 13.7% | |
|
|
| One replication only; the paper's tables average 30 (daily Prophet: 26.0% without a dose, 7.8% with the HESI dose). |
| LightGBM cannot transfer a dose: its trees split the stoppage feature at 1e-35, so any positive dose becomes a full |
| holiday. |
|
|
| ## Reproducibility |
|
|
| `make model` in the code repository refits these models from the simulated series (identical to the paper's |
| replication 0 to machine precision), saves them, reloads them with `hes_forecast.py` and checks that the reloaded |
| forecasts equal the in-memory ones. They were fitted on macOS arm64 with prophet 1.5.0, lightgbm 4.7.0, |
| statsmodels 0.15.0, numpy 2.5.4 and pandas 3.1.1. LightGBM reproduces the paper's run exactly. The Prophet and |
| SARIMAX optimizers land on slightly different solutions on a different platform: the largest monthly difference from |
| the paper's forecasts is 0.1-0.5% for Prophet and 1.4% for SARIMAX, and no MAPE above moves by more than 0.7 |
| points. `forecast_check.csv` lists every month. |
|
|
| ## Licence |
|
|
| CC BY 4.0 (© 2026 Thanatchon Chongmankhong). The models hold only simulated exports and the Thai and Chinese |
| holiday calendars (training doses are zero). The HESI dose in the dataset is derived from Google COVID-19 Community |
| Mobility Reports and remains under Google's terms; see the dataset's licence. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{chongmankhong2026lockdowns, |
| author = {Chongmankhong, Thanatchon}, |
| title = {Lockdowns as Partial Holidays: Identifying Work-Stoppage Elasticities from Recurring Holidays |
| and Transferring Them to Forecast {Thailand}'s Monthly Exports through the {COVID-19} Shock}, |
| year = {2026}, |
| doi = {10.5281/zenodo.24036954}, |
| note = {Preprint; export values are simulated}, |
| url = {https://thanatchon36.github.io/lockdowns-as-partial-holidays/paper/} |
| } |
| ``` |
|
|
