Facebook Prophet Research Articles

OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING

MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.

Exploring PM2.5 and PM10 ML forecasting models: a comparative study in the UAE

Particulate Matters PM and PM present a major health and environmental concern in urban regions. This research compares machine learning and time series models, such as Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Facebook Prophet, for predictions of these matters. Their performances have been evaluated over 1-2 hours, 1 day and 1 week forecasting periods using five years real-life data from six ground stations in Abu Dhabi, UAE. Performance metrics including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Percent Bias (PBIAS) were applied. Linear SVR was generally the best performing model for PM predictions at all stations with averages of 18.7% and 28.2% MAPE for 1 and 2-hour periods, respectively. However, CNN performed best in forecasting PM for 1-hour horizon, with an average MAPE of 12.6%. For the 2-hour forecast, SVR outperformed other models, with 18.3% MAPE. Facebook Prophet consistently outperformed others for both PM and PM with 21.8% and 13.4% MAPE for 1-day and 21.3% and 13.8% MAPE for 1-week, respectively. These best performing models yielded similar RMSE, MAE, and PBIAS values for both PM and PM.

Forecasting Ethanol and Gasoline Consumption in Brazil: Advanced Temporal Models for Sustainable Energy Management

The sustainable management of energy resources is fundamental in addressing global environmental and economic challenges, particularly when considering biofuels such as ethanol and gasoline. This study evaluates advanced forecasting models to predict consumption trends for these fuels in Brazil. The models analyzed include ARIMA/SARIMA, Holt–Winters, ETS, TBATS, Facebook Prophet, Uber Orbit, N-BEATS, and TFT. By leveraging datasets spanning 72, 144, and 263 months, the study aims to assess the effectiveness of these models in capturing complex temporal consumption patterns. Uber Orbit exhibited the highest accuracy in forecasting ethanol consumption among the evaluated models, achieving a mean absolute percentage error (MAPE) of 6.77%. Meanwhile, the TBATS model demonstrated superior performance for gasoline consumption, with a MAPE of 3.22%. Our models have achieved more accurate predictions than other compared works, suggesting ethanol demand is more dynamic and underlining the potential of advanced time–series models to enhance the precision of energy consumption forecasts. This study contributes to more effective resource planning by improving predictive accuracy, enabling data-driven policy making, optimizing resource allocation, and advancing sustainable energy management practices. These results support Brazil’s energy sector and provide a framework for sustainable decision making that could be applied globally.

Forecasting bitcoin price fluctuations: a time series analysis approach for predictive modelling

The recent fluctuations in the price of Bitcoin, marked by both significant increases and subsequent decreases, has attracted media and public attention. Consequently, many researchers have explored various factors influencing Bitcoin’s price and the underlying patterns of its fluctuations. This paper aims to predict and analyses the factors affecting Bitcoin’s price by creating a unique dataset with nearly 40 features and deriving two child datasets using correlation and mutual information as feature selection techniques. Additionally, we train machine learning models, including linear regression (LR), extreme gradient boosting (XGBoost), support vector regression (SVR), Facebook Prophet (FB Prophet), and bidirectional gated recurrent unit (BI-GRU), to predict Bitcoin’s next-day price. The model’s performance is evaluated using mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score metrics. Our findings indicate that machine learning techniques are effective in predicting Bitcoin’s price and could be valuable for investors seeking to maximize profits.

Application of algorithms for forecasting and optimizing the production capacity of wind energy

With advances in technology, particularly where environmental implications are a concern, equipment has become increasingly complex and metered. Human activities like emissions and urbanization significantly impact the atmosphere and wind energy generation. This study predicts wind energy generation capacity for 2025–2029 based on data from 2000–2024. Predictive models (e.g., Long Short-Term Memory (LSTM), Facebook Prophet, and SARIMA) are applied to analyze energy trends. Performance results show LSTM provides the best accuracy. The findings enhance reliable wind energy forecasting and improved energy management methods in response to shifting climate dynamics.

Time-series projecting road traffic fatalities in Australia: Insights for targeted safety interventions.

Despite substantial progress in road safety, road traffic fatalities (RTFs) continue to be a persistent issue in Australia. This study aims to forecast RTFs trends up to 2050 by analyzing factors such as geographic location, age, gender, speed limits, and time of occurrence. Utilizing historical data from 1989 to 2024, fatalities were categorized by road user type, demographics, and day of the week. The Facebook Prophet time series model, incorporating categorical variables like region, age, and speed limits, was employed to predict future trends. The analysis reveals significant regional disparities in fatality reduction rates, with some areas lagging others. Gender-specific forecasts indicate a sharper decline in male fatalities compared to females, while projections highlight persistent risks for older drivers. Additionally, highways with higher speed limits are expected to see a substantial decrease in fatalities. These insights emphasize the need for targeted interventions in areas with slower reductions and high-risk demographic groups, aiding policymakers in refining safety measures, enforcing speed limits, and enhancing public awareness campaigns.

Time series forecasting-based Kubernetes autoscaling using Facebook Prophet and Long Short-Term Memory

The advancement of cloud computing technologies has led to increased usage in application deployment in recent years. Kubernetes, a widely used container orchestration platform for deploying applications on cloud systems, provides benefits such as autoscaling to adapt to fluctuating workload while maintaining quality of service and availability. In this research, we designed and evaluated a proactive Kubernetes autoscaling using Facebook Prophet and Long Short-Term Memory (LSTM) hybrid model to predict the HTTP requests and calculate required pod counts based on the Monitor-Analyze-Plan-Execute loop. The proposed model not only captures seasonal data patterns effectively but also proactively predicts the pod requirements for timely and efficient resource allocation to reduce resource wastage while enhancing cloud computing applications. The proposed hybrid model was evaluated using real-world datasets from NASA and the Federation Internationale de Football Association (FIFA) World Cup to benchmark and compare with existing literature. Evaluation results indicate that the proposed novel hybrid model outperforms single-model proactive autoscaling by a maximum margin of 65–90% accuracy when compared to NASA and FIFA World Cup datasets. This study contributes to the fields of cloud computing and container orchestration by providing a refined proactive autoscaling mechanism that enhances application availability, efficient resource usage, and reduced costs and paves the way for further exploration in increased prediction speed, integrated with vertical scaling and implementations using Kubernetes.

Predicting Olympic Medal Trends for Southeast Asian Countries Using the Facebook Prophet Model

The Olympics is a world sporting event held every four years and is a meeting place for all athletes worldwide. The Olympics are held alternately in different countries. The Olympics were first held in Athens in 1896 and have now reached the 33rd Olympics, which will be held in Paris in 2024. A lot of work has been done to develop prediction models emphasizing improving accuracy to predict Olympic outcomes. However, low-performance regression algorithms are the main problems with prediction. By integrating custom seasonality with the Facebook-Prophet prediction model, this study aims to increase the accuracy of Olympic prediction. The proposed new model involves several steps, including preparing the data and initializing and fitting the Facebook-Prophet model with several parameters such as seasonal mode, annual seasonality, and prior scale. The model is tested using the Olympic dataset (1994–2024). The evaluation results show that this prediction model can provide a good value in predicting the total medals earned. On the Olympic Games (1994-2024) dataset, the model has a very low error MAE, MSE, and RMSE and has an R2 score of 0.99, which is close to perfect. This research shows that the model is effective in improving prediction accuracy.

Prediction and decoding of metaverse coin dynamics: a granular quest using MODWT-Facebook’s prophet-TBATS and XAI methodology

The growing media buzz and industry focus on the emergence and rapid development of Metaverse technology have paved the way for the escalation of multifaceted research. Specific Metaverse coins have come into existence, but they have barely seen any traction among practitioners despite their tremendous potential. The current work endeavors to deeply analyze the temporal characteristics of 6 Metaverse coins through the lens of predictive analytics and explain the forecasting process. The dearth of research imposes serious challenges in building the forecasting model. We resort to a granular prediction setup incorporating the Maximal Overlap Discrete Wavelet Transformation (MODWT) technique to disentangle the original series into subseries. Facebook's Prophet and TBATS algorithms are utilized to individually draw predictions on granular components. Aggregating components-wise forecasted figures achieve the final forecast. Facebook's Prophet is deployed in a multivariate setting, applying a set of explanatory features covering macroeconomic, technical, and social media indicators. Rigorous performance checks justify the efficiency of the integrated forecasting framework. Additionally, to interpret the black box typed prediction framework, two explainable artificial intelligence (XAI) frameworks, SHAP and LIME, are used to gauge the nature of the influence of the predictor variables, which serve several practical insights.

Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman

The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R2, and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R2 values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman.

A Short-Term Prediction Model for the Number of Registered Motor Vehicles Using Facebook Prophet Forecasting Approach

A Short-Term Prediction Model for the Number of Registered Motor Vehicles Using Facebook Prophet Forecasting Approach

An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach.

The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics. Our study provides a novel finding on the prevalence of drug abuse by defining a new subcategory of X content: "tweets that reference established drug lyrics." We aim to investigate drug trends in popular music on X, identify and classify popular drugs, and analyze related artists' gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on X. X data were collected from 2015 to 2017 through the X streaming application programming interface (API). Drug lyrics were obtained from the Genius lyrics database using the Genius API based on drug keywords. The Smith-Waterman text-matching algorithm is used to detect the drug lyrics in posts. We identified famous drugs in lyrics that were posted. Consequently, the analysis was extended to related artists, songs, genres, and popularity on X. The frequency of drug-related lyrics on X was aggregated into a time-series, which was then used to create prediction models using linear regression, Facebook Prophet, and NIXTLA TimeGPT-1. In addition, we analyzed the number of followers of users posting drug-related lyrics to explore user characteristics. We analyzed over 1.97 billion publicly available posts from 2015 to 2017, identifying more than 157 million that matched drug-related keywords. Of these, 150,746 posts referenced drug-related lyrics. Cannabinoids, opioids, stimulants, and hallucinogens were the most cited drugs in lyrics on X. Rap and hip-hop dominated, with 91.98% of drug-related lyrics from these genres and 84.21% performed by male artists. Predictions from all 3 models, linear regression, Facebook Prophet, and NIXTLA TimeGPT-1, indicate a slight decline in the prevalence of drug-related lyrics on X over time. Our study revealed 2 significant findings. First, we identified a previously unexamined subset of drug-related content on X: drug lyrics, which could play a critical role in models predicting the surge in drug-related incidents. Second, we demonstrated the use of cutting-edge time-series forecasting tools, including Facebook Prophet and NIXTLA TimeGPT-1, in accurately predicting these trends. These insights contribute to our understanding of how social media shapes public behavior and sentiment toward drug use.

Comparative Study of Forecasting Methods to Predict the Energy Demand for the Market of Colombia

An important challenge for the energy sector worldwide is the selection of the best method for forecasting the energy demand of a country. This research addresses the Colombian electricity market, a developing country where power generation is predominantly hydroelectric. The goal is to compare commonly used and state-of-the-art methods to predict the general short-term demand. The selected methods are the long short-term memory (LSTM) network, the autoregressive integrated moving average (ARIMA) and Facebook Prophet. These methods were used to predict the hourly total electricity demand for Colombia for the next day (24 h). The cross-industry standard process for data mining methodology was partially used, excluding the deployment phase. The hourly time series considers data from 2022 to 2023. Models are evaluated using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Prophet had the best RMSE with 428.5 MW, followed by ARIMA (441.7 MW) and LSTM (541.3 MW). Prophet and ARIMA achieved the best MAPE (4.1%), outperforming LSTM (4.9%). Results shown that the hourly intraday energy demand of the Colombian market can be predicted accurately for both work and non-work days.

Empirical Comparison of Facebook Prophet and Traditional Models for Tomato Price Forecasting in Greece

Agricultural product prices are crucial to the income and livelihoods of millions worldwide, making accurate forecasting essential for market participants. The European Union regularly reports agricultural product prices through an online databank, ensuring up-to-date price information is widely available. This study evaluates the effectiveness of Facebook Prophet, a modern forecasting tool, in predicting tomato prices in Greece from 2013 to 2024.The results show that Facebook Prophet outperforms traditional forecasting models, providing more accurate predictions with lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values. Notably, the model exhibited superior performance in forecasting tomato prices over a six-month horizon compared to conventional seasonally adjusted models. This demonstrates Facebook Prophet’s potential to significantly improve decision-making in agricultural markets, offering reliable price forecasts to stakeholders such as farmers, traders, and policymakers. Furthermore, the study incorporated feedback from market participants, which provided valuable insights into market practices and conditions. This integration of practical knowledge with advanced forecasting techniques enhanced the interpretation of the results, making them more applicable to real-world scenarios. Overall, the findings suggest that Facebook Prophet holds considerable promise for future agricultural price forecasting, with potential applications across various commodities. These insights pave the way for more precise agricultural forecasts, benefiting all market participants by supporting more informed and timely decision-making.

Trend‐Based Predictive Maintenance and Fault Detection Analytics for Photovoltaic Power Plants

Optimized predictive maintenance in photovoltaic (PV) systems is crucial for ensuring prolonged operational performance and cost‐effective operation and maintenance (O&M). Even though failure detection methods have already been developed, the main challenge remains the lack of predictive maintenance strategies to accurately forecast underperformance conditions. The scope of this work is to develop a predictive maintenance and failure detection routine for assessing the health status of PV systems. The workflow consists of the eXtreme gradient boosting algorithm for modeling the PV performance, the one‐class support vector machine algorithm for fault detection, and the Facebook Prophet algorithm for forecasting PV performance trends and generating maintenance alerts. The developed data‐driven routine analyzes performance trend deviations and it is validated using a historical dataset from a utility‐scale PV power plant in Greece. The obtained results show the effectiveness of the developed workflow in detecting fault conditions, achieving a sensitivity of 96.9%. Additionally, the results demonstrate the workflow's ability to generate predictive maintenance alerts up to 7 days in advance, yielding a sensitivity of 92.9%. Finally, the study provides useful insights that enhance operators’ efficiency in conducting O&M activities.

Integrating Twitter Sentiment with ML Models for Enhanced Stock Forecasting

This research investigates the integration of Twitter sentiment analysis with machine learning models, specifically Long Short-Term Memory (LSTM), Facebook Prophet, and Neural Prophet, to enhance the accuracy of stock market predictions. While these models are accurate in capturing long-term trends and seasonality, they often fail when exploring the short-term volatility in stock prices. By using real-time social media sentiments, we aim to improve short-term prediction accuracy. Sentiment data from Twitter, extracted and classified using NLP techniques, was integrated with the models to predict stock prices for Apple Inc. (AAPL), Paramount Global (PARA), and BP plc (BP) over the period 2016-2024. The study compares the performance of the models using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results show that Neural Prophet stood the best in terms of its accuracy and efficiency in stock predictions when compared to LSTM and Facebook Prophet, reducing RMSE by 25% and MAE by 20%. While the integration of sentiment analysis improved prediction accuracy, limitations remain in predicting the scale of sudden market changes. The findings highlight the potential of sentiment analysis in stock prediction but also call for further model enhancements to fully address short-term market volatility.

Time Series Forecasting of Air Quality Index in Lahore : A Machine Learning Perspective with Facebook Prophet Model

This research investigates the rising problem of air pollution, which is a serious issue affecting people's health and the environment on a global scale. Focusing on Lahore, Pakistan, a city facing severe pollution, the research examines the Air Quality Index (AQI) from May 2019 to December 2023. Using the Facebook Prophet model for time-series forecasting, the analysis reveals trends at monthly, weekly, and hourly intervals. Peak AQI concentrations are identified in winter months, notably December and January, aligning with hazardous smog levels. Weekly trends show minimum AQI on weekends, particularly Saturday and Sunday. The hourly trend points to peak AQI concentrations between 7:00 and 09:00 AM, correlating with morning traffic and emphasizing the role of vehicular emissions in air quality degradation. These forecasts are crucial for anticipating and mitigating the adverse impact of air pollution on human health. This research underscores the critical need for comprehensive strategies to address air pollution, acknowledging the Lahore High Court and Government's initiatives, such as artificial rain experimentation, closure of commercial markets by 10 PM, ban on cutting trees and smoke-emitting vehicles. In summary, this research significantly contributes to understanding and tackling the increasingly urgent challenges associated with air quality.

Assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through time series analysis

Climate change, characterized by long-term shifts in global or regional weather patterns, is a consequence of natural processes and human activities. These shifts encompass al-terations in temperature, precipitation, wind patterns, and other climatic variables, all of which exert direct influence on crop growth, development, and overall agricultural productivity. Comprehending the intricate relationship between climate change and crop production is par-amount for formulating strategies to counteract adverse effects and adapt to evolving conditions. This study focuses on assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through rigorous time series analysis. The research involved the application of various time series models, including SARIMA, ARIMA, Facebook Prophet, LSTM, VAR, and Multiple Linear Regression. The investigation began with forecasting temperature patterns using SARIMA and Facebook Prophet models. SARIMA outperformed Facebook Prophet in this context, as evidenced by lower RMSE and MSE metrics. Subsequently, the study delved into predicting rainfall and precipitation, employing ARIMA and LSTM models. In this case, LSTM demonstrated superior predictive capabilities. Finally, wheat production yield was analyzed using VAR and Multiple Linear Regression, with VAR yielding more accurate predictions. The findings of this study hold profound implications for policymakers, farmers, and stakeholders deeply invested in agriculture and food security. By shedding light on the repercussions of climate change on crop production through the application of time series analysis, this project aspires to contribute to developing sustainable agricultural practices, robust farming systems and proactive policies de-signed to mitigate the adverse effects of climate change on global food production.

How Effective Are Forecasting Models in Predicting Effects of Exoskeletons on Fatigue Progression?

Forecasting can be utilized to predict future trends in physiological demands, which can be beneficial for developing effective interventions. This study implemented forecasting models to predict fatigue level progression when performing exoskeleton (EXO)-assisted tasks. Specifically, perceived and muscle activity data were utilized from nine recruited participants who performed 45° trunk flexion tasks intermittently with and without assistance until they reached medium-high exertion in the low-back region. Two forecasting algorithms, Autoregressive Integrated Moving Average (ARIMA) and Facebook Prophet, were implemented using perceived fatigue levels alone, and with external features of low-back muscle activity. Findings showed that univariate models without external features performed better with the Prophet model having the lowest mean (SD) of root mean squared error (RMSE) across participants of 0.62 (0.24) and 0.67 (0.29) with and without EXO-assisted tasks, respectively. Temporal effects of BSIE on delaying fatigue progression were then evaluated by forecasting back fatigue up to 20 trials. The slope of fatigue progression for 20 trials without assistance was ~48-52% higher vs. with assistance. Median benefits of 54% and 43% were observed for ARIMA (with external features) and Prophet algorithms, respectively. This study demonstrates some potential applications for forecasting models for workforce health monitoring, intervention assessment, and injury prevention.

COMPARISON OF VISITS TO THE FACEBOOK WEBSITE USING THE FACEBOOK PROPHET AND SARIMA MODELS

Facebook is widely used across various segments of society and offers significant potential as a marketing tool due to its diverse content and communication features. This study aims to forecast visits to the Facebook website for the period of March 2023 to February 2024 using two forecasting methods: the Facebook Prophet and SARIMA models. We analyzed 167 data points representing the percentage of visits to the Facebook website from April 2009 to February 2023, sourced from Statcounter. Our forecasting results indicate that the Facebook Prophet model yielded a Mean Absolute Percentage Error (MAPE) of 22.3% and a Root Mean Square Error (RMSE) of 16.2%. In comparison, the SARIMA model achieved a MAPE of 6.31% and an RMSE of 5.02%. Based on these accuracy metrics, the SARIMA model is determined to be more suitable for forecasting the percentage of visits to the Facebook social media website in Indonesia. This finding underscores the importance of selecting appropriate forecasting models for accurate predictions in social media analytics