Prophet model beats classic forecasts for Ghana’s volatile rainfall

Rainfall in Ghana’s Western Region can swing from a life-giving monsoon downpour to a destructive flood within a single season, and predicting those swings has long been one of the hardest problems in West African meteorology. A new study published in BMC Environmental Science by researchers at Takoradi Technical University suggests that a forecasting tool originally developed inside a social media company may outperform decades of classical statistical machinery. The team, led by Senyefia Bosson-Amedenu, compared five time series models on 84 months of monthly rainfall data and found that Facebook Prophet, an additive regression framework released by Meta’s data science group, consistently beat SARIMA, exponential smoothing, TBATS and a hybrid ARIMA-plus-ETS approach on nearly every metric they tested.

The stakes are far from academic. The Western Region, wedged between Côte d’Ivoire and the Gulf of Guinea, receives between 1,500 and 2,000 millimetres of rain each year, sustaining cocoa farms, timber plantations and oil palm groves that anchor the local economy. Its coastal capital, Sekondi-Takoradi, has repeatedly seen heavy rainfall overwhelm drainage systems, and flooding remains a chronic threat. Better forecasts translate directly into better decisions about when to plant, when to harvest, how much water to store, and when to warn communities that a flood may be coming.

The researchers drew on validated monthly records from the Ghana Meteorological Agency spanning January 2017 to December 2023, with individual monthly totals ranging from zero to 419 millimetres. Before any modelling began, they confirmed that the series was statistically stationary using both the augmented Dickey-Fuller test, which returned a statistic of -4.34 with a p-value of 0.01, and the KPSS test, which failed to reject stationarity at the 0.1 level. That dual confirmation matters because models like SARIMA assume a stable mean and variance; if the underlying data drift, the forecasts inherit the drift as error.

Extreme values posed a subtler problem. A pronounced rainfall spike around 2020, likely tied to extreme weather, threatened to distort parameter estimates for every model. The team tested two remedies: winsorization, which caps extreme values at the 5th and 95th percentiles while keeping them in the dataset, and the interquartile range method, which effectively trims outliers away. Winsorization proved the better tool. It preserved the central tendency of the data, holding the mean at 94.24 millimetres against the original 97.43, while taming the standard deviation from 95.93 to 86.65. The IQR method pushed the mean down to 83.67 and, crucially, discarded information about genuine extreme events. For a forecasting problem where the extremes are precisely what you want to anticipate, that distinction is decisive.

Prophet’s architecture explains much of its advantage. The model decomposes a time series into a trend component, a seasonal component modelled as a Fourier series, a holiday or event component, and a noise term. Its trend can follow either a piecewise linear path with automatically detected change points or a logistic growth curve with a carrying capacity, allowing it to bend when the data bend rather than forcing the data into a fixed seasonal structure. SARIMA, by contrast, relies on autoregressive and moving average terms with rigid seasonal orders, which the authors note struggles with the nonlinear, abrupt shifts characteristic of tropical rainfall. ETS smoothing methods and TBATS, which handles multiple seasonal cycles through trigonometric terms, fared better in some respects but still lagged behind.

The performance gap was substantial. Prophet achieved the lowest Akaike information criterion at 880.09 and Bayesian information criterion at 884.95, alongside a mean squared error of 1,981.79 and a mean absolute error of 35.15. SARIMA, the traditional workhorse, posted an AIC of 947.18, an MSE of 3,590.58 and an MAE of 46.15. The hybrid ARIMA-plus-ETS model landed in between, with an AIC of 1,005.93, while TBATS and ETS trailed further. On explanatory power, Prophet reached an R-squared of 0.92 with a mean absolute percentage error of 4.8 percent, compared with 0.85 and 7.3 percent for SARIMA, 0.79 and 8.6 percent for ETS, and just 0.76 and 9.2 percent for TBATS. When winsorization was applied, Prophet’s R-squared climbed from 0.89 to 0.92 and its MAPE fell from 5.6 to 4.8 percent, whereas IQR-based removal degraded performance by discarding data.

Residual diagnostics reinforced the case. A Box-Ljung test on the model’s residuals returned a p-value of 0.1055, indicating no significant autocorrelation and suggesting the model had captured the systematic structure of the series. Standardized residual plots showed most errors clustering near zero, though spikes around 2019 and 2021 hinted at anomalies, possibly extreme weather events, that no model fully absorbed. The fitted model’s parameters told a story of a rainfall regime in slow flux: a small positive growth rate of roughly 0.00036 per month, negligible seasonal coefficients, and change-point values so small that the authors concluded the overall trend has remained remarkably stable despite year-to-year drama.

Looking forward, the model’s 2024 forecast projected the familiar bimodal pattern with peaks exceeding 200 millimetres in May and June, including a May prediction of 224.47 millimetres within a confidence interval stretching from 173.18 to 279.09 millimetres. On the fitting sample, Prophet posted an R-squared of 0.9999895, a Theil’s U statistic of 0.0013 and a MAPE of just 1.116 percent, figures the authors interpret as evidence that the model captures nearly all variance without overfitting, since the adjusted R-squared differed only marginally. The wide confidence intervals during peak months, however, are a reminder that uncertainty balloons exactly where the consequences are largest.

The authors are candid about limitations. Prophet assumes a degree of climate stationarity, the expectation that historical patterns will persist, which accelerating climate change may erode. Monthly data, moreover, can hide the short-duration cloudbursts that matter most for flash flood warning. They argue that future systems should fuse Prophet with machine learning anomaly detection, such as isolation forests or neural classifiers, and with high-resolution geospatial data to move from regional averages to village-scale advisories. Deploying such tools nationally would demand investment in training, real-time data pipelines and infrastructure, costs the authors frame as small against the losses from missed floods and mistimed harvests.

What emerges is a quietly radical suggestion for operational meteorology in data-scarce regions: the most flexible tool won, and it won by respecting the messiness of the data rather than disciplining it. Earlier work in the same region using an ARMA model explained only 53 percent of rainfall variation; even a recent SARIMA effort with careful outlier adjustment reached an R-squared of 99.02 percent on its fitting sample but required extensive manual preprocessing. Prophet’s automated handling of trend shifts, seasonality and outliers offers a scalable path for Ghana’s National Climate Change Policy ambitions, from flood early warning systems in Takoradi to irrigation scheduling across the cocoa belt. As the authors put it in their conclusion, models that generalize well and account for nonlinearity and seasonality are the ones that will help climate-sensitive societies stay ahead of the rain.

Subject of Research: Comparative time series modelling of monthly rainfall forecasting in Ghana's Western Region

Article Title: Advanced time series modelling to address seasonal variability and extreme rainfall in Ghana

Article References: Bosson-Amedenu, S., Baah, E. M., Ayiah-Mensah, F., & Addor, J. A. (2025). Advanced time series modelling to address seasonal variability and extreme rainfall in Ghana. BMC Environmental Science, 2(1), Article 8. https://doi.org/10.1186/s44329-025-00024-8

Image Credits: AI Generated

DOI: 10.1186/s44329-025-00024-8

Keywords: rainfall forecasting, Facebook Prophet, SARIMA, time series analysis, Ghana, Western Region, outlier handling, winsorization, climate resilience, seasonality, flood early warning, BMC Environmental Science