Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study

Christopher J Lynch; Ross Gore

doi:10.2196/24925

Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study

Journal of Medical Internet Research ◽

10.2196/24925 ◽

2021 ◽

Vol 23 (3) ◽

pp. e24925 ◽

Cited By ~ 2

Author(s):

Christopher J Lynch ◽

Ross Gore

Keyword(s):

Early Onset ◽

Short Range ◽

Moving Average ◽

New York Times ◽

Health District ◽

Percentage Error ◽

Short Term ◽

The Public ◽

Forecasting Methods ◽

Case Count

Background Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. Objective COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior–day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. Methods We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia’s 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts. Results The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days’ counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels. Conclusions For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v9i2.563 ◽

2019 ◽

Vol 9 (2) ◽

Author(s):

Rendra Gustriansyah ◽

Wilza Nadia ◽

Mitha Sofiana

Keyword(s):

Moving Average ◽

Exponential Smoothing ◽

Standard Type ◽

The Public ◽

Forecasting Methods ◽

Forecasting Method ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Single Exponential

AbstractHotel is a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.Keywords: forecasting, linier regression, moving average, exponential smoothing.AbstrakHotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.Kata kunci: peramalan, regeresi linier, moving average, exponential smoothing.

Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

Journal of Physics Conference Series ◽

10.1088/1742-6596/2123/1/012044 ◽

2021 ◽

Vol 2123 (1) ◽

pp. 012044

Author(s):

Sukarna ◽

Elma Yulia Putri Ananda ◽

Maya Sari Wahyuni

Keyword(s):

Kalman Filter ◽

Moving Average ◽

Arima Model ◽

Rainfall Data ◽

Percentage Error ◽

Filter Method ◽

Rainfall Forecasting ◽

Forecasting Methods ◽

Filter Methods ◽

Kalman Filter Method

Abstract Many forecasting methods have been used for forecasting rainfall data. Kalman Filter is one of the forecasting methods that could give better forecasts. To our knowledge, the Kalman Filter method has not been used to forecast rainfall data in Makassar, Indonesia. This study aims to provide more precise forecasts for rainfall data in Makassar, Indonesia by using Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter methods. Rainfall data from January 2010 to December 2020 were used. The best model selection is based on the smallest Mean Absolute Percentage Error (MAPE) value. The results showed that the best ARIMA model is ARIMA(0,1,1)(0,1,1)12 with MAPE is 111.48, while MAPE value by using the Kalman Filter algorithm is 47.00 indicating that Kalman Filter has better prediction than ARIMA model.

Peramalan Penjualan Olein Curah di Perusahaan Pengolahan Kelapa Sawit Menggunakan Double Moving Average

INVENTORY: Industrial Vocational E-Journal On Agroindustry ◽

10.52759/inventory.v2i2.53 ◽

2021 ◽

Vol 2 (2) ◽

pp. 39

Author(s):

Nurike Oktavia ◽

Alya Agustina ◽

Ridha Luthvina

Keyword(s):

Data Processing ◽

Palm Oil ◽

Moving Average ◽

Percentage Error ◽

Oil Processing ◽

Cooking Oil ◽

Forecasting Error ◽

Forecasting Methods ◽

Calculation Results ◽

Oil Demand

Bulk olein is one of the products produced by Palm Oil Processing Company. Bulk cooking oil controls 75 percent of the production market share in Indonesia and about 77.5 percent of households in Indonesia use bulk cooking oil because the price is cheaper than packaged cooking oil. Demand for olein in the future is predicted to be continued to increase, so it is necessary to estimate future sales so that production activities become more effective and efficient. The method used in this study is the double moving average (DMA), which is one of the forecasting methods with data that has a trend. The calculation will be done by comparing the result of 3 moving, 4 moving and 5 moving. Forecasting error is calculated using mean absolute percentage error (MAPE). The calculation results show that the average MAPE from DMA with 5 moving has the smallest value. To verify these results, an analysis of the processed data was carried out, namely looking for data with the furthest distance from the linear line, namely t3 data and t7 data. The data is omitted in data processing and then the MAPE error value is recalculated. The results obtained are that DMA with 3 moving results have the smallest error, which is 11.863 percent. For this reason, the chosen forecasting calculation is a double moving average with 3 moving.

Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries

10.1101/2020.08.03.20167189 ◽

2020 ◽

Author(s):

Ghufran Ahmad ◽

Furqan Ahmed ◽

Suhail Rizwan ◽

Javed Muhammad ◽

Hira Fatima ◽

...

Keyword(s):

Moving Average ◽

Data Availability ◽

Data Driven ◽

Percentage Error ◽

Pictorial Representation ◽

Limited Data ◽

Autoregressive Integrated Moving Average ◽

Forecasting Methods ◽

Global Pandemic ◽

Future Burden

AbstractThe WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries, and has been declared as a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 173 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), random walk forecasts (RWF) with and without drift. We also evaluate the accuracy of these forecasts using the Mean Absolute Percentage Error (MAPE). The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generated heat maps to provide a pictorial representation of the countries at risk of having an increase in cases in the coming 4 weeks for June. Due to limited data availability during the ongoing pandemic, less data-hungry forecasting models like ARIMA and ETS can help in anticipating the future burden of SARS-CoV2 on healthcare systems.

Online SARIMA applied for short-term electricity load forecasting

10.21203/rs.3.rs-439120/v1 ◽

2021 ◽

Author(s):

Quang Dat Nguyen ◽

Nhat Anh Nguyen ◽

Ngoc Thang Tran ◽

Vijender Kumar Solanki ◽

Rubén González Crespo ◽

...

Keyword(s):

Power Systems ◽

Moving Average ◽

Supply And Demand ◽

Load Forecasting ◽

Percentage Error ◽

Short Term ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Short Term Load Forecasting ◽

Electricity Load

Abstract Short-term Load Forecasting (STLF) plays a crucial role in balancing supply and demand of load dispatching operation, ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%.

Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models

Energies ◽

10.3390/en11112976 ◽

2018 ◽

Vol 11 (11) ◽

pp. 2976 ◽

Cited By ~ 12

Author(s):

Qinkai Han ◽

Hao Wu ◽

Tao Hu ◽

Fulei Chu

Keyword(s):

Neural Network ◽

Wind Speed ◽

Nonlinear Models ◽

Moving Average ◽

Back Propagation ◽

Percentage Error ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Short Term ◽

Wind Speed Forecasting

Accurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and hybrid linear/nonlinear models. Original wind speed series is decomposed into a finite number of intrinsic mode functions (IMFs) and residuals by using the EMD. Several popular linear and nonlinear models, including autoregressive integrated moving average (ARIMA), support vector machine (SVM), random forest (RF), artificial neural network with back propagation (BP), extreme learning machines (ELM) and convolutional neural network (CNN), are utilized to study IMFs and residuals, respectively. An ensemble forecast for the original wind speed series is then obtained. Various experiments were conducted on real wind speed series at four wind sites in China. The performance and robustness of various hybrid linear/nonlinear models at two time intervals (10 min and 1 h) are compared comprehensively. It is shown that the EMD based hybrid linear/nonlinear models have better accuracy and more robust performance than the single models with/without EMD. Among the five hybrid models, EMD-ARIMA-RF has the best accuracy on the whole for 10 min data, and the mean absolute percentage error (MAPE) is less than 0.04. However, for the 1 h data, no model can always perform well on the four datasets, and the MAPE is around 0.15.

Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases

PLoS ONE ◽

10.1371/journal.pone.0252147 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0252147

Author(s):

Ghufran Ahmad ◽

Furqan Ahmed ◽

Muhammad Suhail Rizwan ◽

Javed Muhammad ◽

Syeda Hira Fatima ◽

...

Keyword(s):

Moving Average ◽

Absolute Error ◽

Data Availability ◽

Data Driven ◽

Percentage Error ◽

Pictorial Representation ◽

Short Term ◽

Autoregressive Integrated Moving Average ◽

Global Pandemic ◽

Short Term Forecasting

Background The WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries and has been declared a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. Methodology This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 187 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), and random walk forecasts (RWF) with and without drift. For these forecasts, we evaluate the accuracy and systematic errors using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), respectively. Findings The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generate heat maps to provide a pictorial representation of the countries at risk of having an increase in the cases in the coming 4 weeks of February 2021. Conclusion Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS, can help in anticipating the future outbreaks of SARS-CoV2.

Accuracy of Moving Average Forecasting for NEPSE

Journal of Nepalese Business Studies ◽

10.3126/jnbs.v13i1.34706 ◽

2020 ◽

Vol 13 (1) ◽

pp. 62-76

Author(s):

Rashesh Vaidya

Keyword(s):

Stock Market ◽

Graphical Representation ◽

Moving Average ◽

Annual Reports ◽

Percentage Error ◽

Moving Averages ◽

Short Term ◽

Time Duration ◽

The Future

A simple moving average is one of the oldest and the simplest techniques of forecasting the trends of the stock market. The technical analysts follow mainly three types of moving averages, namely; simple, weighted, and exponential moving averages. Among these three types, as per the interest of investors, short-term and long-term time duration is used to calculate the trend using the moving average. All the mentioned moving averages are used by investors or analysts to predict the future trends of the market using historical data. Hence, for evaluating their forecasting accuracy, the paper has used both the short-term and the long-term moving average. The paper has used the NEPSE (closing) index values to calculate as well as plotted the moving averages to forecast the future trend and its accuracy with the help of Mean Absolute Percentage Error (MAPE). The paper found that there is a better crossover in the graphical representation of the moving average in the long-term moving average. In context to the Nepalese stock market, the MAPE results reflected a weekly (5-trading days) 5-SMA analysis of the market movement as the most relevant in short-term forecasting. Similarly, using the technique of moving average, 200-SMA (200-trading days of a year) was seen as the most effective to forecast long-term trends. The result of the long-term moving average MAPE pointed out that the annual reports of the listed companies better determine the trend of the market.

INFANT POLIO IMMUNIZATION

The Professional Medical Journal ◽

10.29309/tpmj/2015.22.04.1337 ◽

2015 ◽

Vol 22 (04) ◽

pp. 507-513

Author(s):

Muhammad Imran ◽

Jamal Abdul Nasir ◽

Syed Arif Ahmed Zaidi

Keyword(s):

Time Series ◽

Moving Average ◽

Immunization Coverage ◽

Absolute Error ◽

Percentage Error ◽

Forecast Errors ◽

Short Term ◽

Forecasting Models ◽

Polio Immunization ◽

Reported Data

Poliomyelitis is a highly infectious disease but preventable by effective vaccines.Children under five year of age affected by this disease as a result a permanent paralysis.Objectives: To uncover the trend of infant polio immunization coverage through modeling isa significant concern to formulate an adequate vaccination strategies and program after theoutbreak of new cases of polio in a recent year in Pakistan. Design: The reported data ofmonthly infant polio immunization coverage to National Institute of Health, Islamabad, Pakistanfrom January 2008 to July 2013 for the present study has been taken from Pakistan bureau ofstatistics with total time series entities 67. National Institute of Health, Islamabad took the recordof per month number of doses administered ( 0-11 months )children by the registered healthcentre in pakistan. Period: January 2008 - July 2013. Setting: Pakistan bureau of statistics(Statistics House) Methods: A set of various short term time series forecasting models namelyBox-Jenkins, single moving average, double moving average, single parameter exponentialsmoothing, brown, Holts and winter models were carried out to expose the infant polioimmunization coverage trend. Results: Among the several forecasting models ARIMA modelsare chosen due to lower measure of forecast errors namely root mean square error (RMSE),mean absolute error (MAE) and mean absolute percentage error (MAPE). ARIMA (2,1,1), ARIMA(1,0,2), ARIMA (0,1,2) and ARIMA (2,1,1) models are established as an adequate models for theprediction of OPV-0, OPV-1, OPV-2 and OPV-3 respectively. Conclusions: With the exceptionof OPV-1 the infant polio immunization coverage is expected to rise in Pakistan.

Container Shipment Demand Forecasting in the Australian Shipping Industry: A Case Study of Asia–Oceania Trade Lane

Journal of Marine Science and Engineering ◽

10.3390/jmse9090968 ◽

2021 ◽

Vol 9 (9) ◽

pp. 968

Author(s):

Ayesha Ubaid ◽

Farookh Hussain ◽

Muhammad Saqib

Keyword(s):

Mean Squared Error ◽

Moving Average ◽

Demand Forecasting ◽

Import Demand ◽

Percentage Error ◽

Container Shipping ◽

Shipping Industry ◽

Short Term ◽

Container Shipment

Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and marketing data. However, the use of demand forecasting has little been studied in the container shipping industry. Improved visibility into the demand for container shipments has been a long-held objective of industry stakeholders. This paper addresses the shortcomings of both short-term and long-term shipment demand forecasting for the Australian container shipping industry. In this study, we compare three forecasting models, namely, the seasonal auto-regressive integrated moving average (SARIMA), Holt–Winters’ seasonal method and Facebook’s Prophet, to find the best fitting model for short-term and long-term import demand forecasting in the Australian shipping industry. Demand data from three years, i.e., 2016–2018, is used for the Asia–Oceania trade lane. The mean absolute percentage error (MAPE), root mean squared error (RMSE) and 2-fold walk-forward cross-validation are used for the model evaluation. The experiment results observed from the selected metrics suggest that Prophet outperforms the other models in its comparison for container shipment demand forecasting.