Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Žunić, Emir; Korjenic, Kemal; Hodžić, Kerim; Ðonko, Dženana

doi:10.5121/ijcsit.2020.12203

Cited by 47 publications

(33 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Prophet, Facebook's automatic forecasting procedure, has recently been used, among others, in bitcoin prize prediction [19], the forecasting of COVID-19 daily cases in Bangladesh [38] and sales forecasting [73].…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Contemporary Methods on Univariate Time Series Forecasting

Karanikola

Liapis

Kotsiantis

2021

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

In data science, time series forecasting is the process of utilizing past or present (known) observations of a target variable to make predictions about future (unknown) observations. Due to the usefulness of forecasting applications in numerous real-life problems, various Statistical and Machine Learning forecasting models have been proposed over recent years. The purpose of this chapter is to compare the performance of several contemporary forecasting models that are considered state of the art. These include Autoregressive Integrated Moving Average (ARIMA), Neural Basis Expansion Analysis (NBEATS), Probabilistic Time Series Modeling focusing on deep learning-based models and others. In the first section of this work a brief theoretical background of the methods is provided. Then, the experimental procedure is being described. For the comparison, 40 univariate time series of financial data that cover a 1-year period were used. A python repository of automated time series forecasting models (AtsPy) was exploited to run the experiments. For the final comparison three different metrics (RMSE, MAE and MAPE) were taken into consideration. The results of this extended experimental procedure are presented through various explanatory diagrams of the methods' performance in the final section.

show abstract

Section: Related Workmentioning

confidence: 99%

A Comparison of Contemporary Methods on Univariate Time Series Forecasting

Karanikola

Liapis

Kotsiantis

2021

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Where g(t) logistic growth curve for modeling non periodic changes in time series. s(t) is periodic changes, h(t) is effectiveness of yearly seasonally holidays and e(t) is error term accounts unusual changes not accommodated by model [29,30]. The other alternative techniques available for forecasting the GDP are also taken into reference while taking this study [31].…”

Section: Prophetmentioning

confidence: 99%

Using Time Series Forecasting for Analysis of GDP Growth in India

Hassan¹,

Mirza²

2021

IJEME

View full text Add to dashboard Cite

Gross Domestic Product is one of the most important economic indicators of the country and its positive or negative growth indicates the economic development of the country. It is calculated quarterly and yearly at the end of the financial year. The GDP growth of India has seen fluctuations from last few decades after independence and reached as high as 10.25 in 2010 and declined to low of -5.23 in 1979. The GDP growth has witnessed a continuous decline in the past five years, taking it from 8.15 in 2015 to 1.87 in 2020.The lockdown imposed in the country to curb the spread of COVID-19 has caused massive slowdown in the economy of the country by affecting all major contributing sectors of the GDP except agricultural sector. To keep on track on the GDP growth is one of the parameters for deciding the economic policies of the country. In this study, we are analyzing and forecasting the GDP growth using the time series forecasting techniques Prophet and Arima model. This model can assist policy makers in framing policies or making decisions.

show abstract

“…The proposed model performed extremely well for sales prediction with less computing time and memory resources. The paper [6] presented a framework based on Facebook's Prophet algorithm and backtesting strategy for forecasting future sales in the retail industry.…”

Section: Related Workmentioning

confidence: 99%

Impact of time series clustering on fuel sales prediction results

Henzel¹,

Bularz²,

Sikora³

2021

Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

View full text Add to dashboard Cite

The purpose of the paper is to check the impact of data clustering in the process of predicting demand. We checked different ways of adding information about similar datasets to the forecasting process and we grouped the measurements in multiple ways. The experiments were executed on 50 time series describing fuels sales (gasoline and diesel sales) on 25 petrol stations from an international company. We described the data preparation process and feature extraction process. In the 9 presented experiments, we used the XGBoost algorithm and some typical time series forecasting methods (ARIMA, moving average). We showed a case study for two datasets and we discussed the practical usage of the tested solutions. The results showed that the solution which used XGBoost model utilising data gathered from all available petrol stations, in general, worked the best and it outperformed more advanced approaches as well as typical time series methods.

show abstract

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Cited by 47 publications

References 22 publications

A Comparison of Contemporary Methods on Univariate Time Series Forecasting

A Comparison of Contemporary Methods on Univariate Time Series Forecasting

Using Time Series Forecasting for Analysis of GDP Growth in India

Impact of time series clustering on fuel sales prediction results

Contact Info

Product

Resources

About