ales forecasting is the process of predicting future sales for a business or organization. Accurate sales forecasting can help businesses make informed decisions about inventory management, staffing, and overall strategy. Sparkflows provides a range of tools and frameworks for building machine learning models that can be used for sales forecasting.
To build a sales forecasting model using Sparkflows, you would typically follow these steps:
Data preparation: Gather and preprocess your sales data, which typically includes historical sales data, seasonality data, and other relevant data such as pricing and promotions.
Feature selection: Select the most relevant features that are likely to impact sales, such as time of year, marketing campaigns, and changes in pricing.
Model selection: Choose a machine learning algorithm to use for predicting sales, such as linear regression, time series forecasting, or neural networks.
Model training: Use historical sales data to train your model on examples of past sales trends.
Model evaluation: Evaluate the performance of your model using a set of metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).
Deployment: Once you have a model that performs well, you can deploy it to your production environment to make predictions on new sales data.
Sparkflows provides a variety of tools and frameworks that can help with each of these steps, including data preprocessing and transformation tools, feature selection algorithms, and machine learning libraries such as Apache Spark MLlib. Additionally, Sparkflows allows you to automate the entire workflow using pipelines, which can be useful for large-scale, complex projects.
One of the key challenges in sales forecasting is accounting for seasonality, or the regular patterns of sales that occur over time. Sparkflows provides several tools that can help with this, such as time series forecasting models and seasonal decomposition algorithms that can identify and account for seasonal trends in your sales data.
Another important aspect of sales forecasting is feature selection, or identifying which variables are likely to have the most impact on sales. Sparkflows provides a range of feature selection algorithms that can help you identify the most relevant variables, such as correlation-based feature selection and recursive feature elimination.
Overall, Sparkflows provides a comprehensive set of tools and frameworks for building accurate sales forecasting models, and can be a powerful tool for businesses looking to make informed decisions based on their sales data.
For more details visit - https://www.sparkflows.io/
S&OP Planning & Optimization Solution, that integrates Predictive Forecasting, Demand & Supply Planning functionality, powered by SAP Analytics Cloud, with a unique Simulation & Optimization engine, to help solve Supply Planning bottlenecks. This is a Fast-to-Value cloud solution, that can be implemented in just weeks, and provides the simplicity and agility necessary today, to quickly adapt to market challenges, all with a lower cost and lower risk than any traditional software implementations. The cloud technology platform is supported by SAP, but our solution integrates with any ERP/MRP, from SAP and non-SAP systems.
Big Data & Analytics to Improve Supply Chain and Business PerformanceBristlecone SCC
Prof. David Simchi Levi, Engineering Systems Professor at MIT and Chairman of OPS Rules spoke at Bristlecone Pulse 2017 about delivering customer value through digitization, analytics and automation.
[DSC Europe 22] Data-driven transformation: Use case in demand forecasting @ ...DataScienceConferenc1
DataLab is a newly founded (since february 2022) centre of excellence for all topics related to data and data analytics within the Fortenova Group. This presentation will be two-fold. In the first part, we will talk about the organization, its structure, evolvement, goals and recruitment. In the second, we will present a case study on demand forecasting in companies Konzum and Mercator, describing all the necessary steps: migrating the data from local data warehouses to cloud, data preprocessing, modelling, evaluation, and industrialization.
ales forecasting is the process of predicting future sales for a business or organization. Accurate sales forecasting can help businesses make informed decisions about inventory management, staffing, and overall strategy. Sparkflows provides a range of tools and frameworks for building machine learning models that can be used for sales forecasting.
To build a sales forecasting model using Sparkflows, you would typically follow these steps:
Data preparation: Gather and preprocess your sales data, which typically includes historical sales data, seasonality data, and other relevant data such as pricing and promotions.
Feature selection: Select the most relevant features that are likely to impact sales, such as time of year, marketing campaigns, and changes in pricing.
Model selection: Choose a machine learning algorithm to use for predicting sales, such as linear regression, time series forecasting, or neural networks.
Model training: Use historical sales data to train your model on examples of past sales trends.
Model evaluation: Evaluate the performance of your model using a set of metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).
Deployment: Once you have a model that performs well, you can deploy it to your production environment to make predictions on new sales data.
Sparkflows provides a variety of tools and frameworks that can help with each of these steps, including data preprocessing and transformation tools, feature selection algorithms, and machine learning libraries such as Apache Spark MLlib. Additionally, Sparkflows allows you to automate the entire workflow using pipelines, which can be useful for large-scale, complex projects.
One of the key challenges in sales forecasting is accounting for seasonality, or the regular patterns of sales that occur over time. Sparkflows provides several tools that can help with this, such as time series forecasting models and seasonal decomposition algorithms that can identify and account for seasonal trends in your sales data.
Another important aspect of sales forecasting is feature selection, or identifying which variables are likely to have the most impact on sales. Sparkflows provides a range of feature selection algorithms that can help you identify the most relevant variables, such as correlation-based feature selection and recursive feature elimination.
Overall, Sparkflows provides a comprehensive set of tools and frameworks for building accurate sales forecasting models, and can be a powerful tool for businesses looking to make informed decisions based on their sales data.
For more details visit - https://www.sparkflows.io/
S&OP Planning & Optimization Solution, that integrates Predictive Forecasting, Demand & Supply Planning functionality, powered by SAP Analytics Cloud, with a unique Simulation & Optimization engine, to help solve Supply Planning bottlenecks. This is a Fast-to-Value cloud solution, that can be implemented in just weeks, and provides the simplicity and agility necessary today, to quickly adapt to market challenges, all with a lower cost and lower risk than any traditional software implementations. The cloud technology platform is supported by SAP, but our solution integrates with any ERP/MRP, from SAP and non-SAP systems.
Big Data & Analytics to Improve Supply Chain and Business PerformanceBristlecone SCC
Prof. David Simchi Levi, Engineering Systems Professor at MIT and Chairman of OPS Rules spoke at Bristlecone Pulse 2017 about delivering customer value through digitization, analytics and automation.
[DSC Europe 22] Data-driven transformation: Use case in demand forecasting @ ...DataScienceConferenc1
DataLab is a newly founded (since february 2022) centre of excellence for all topics related to data and data analytics within the Fortenova Group. This presentation will be two-fold. In the first part, we will talk about the organization, its structure, evolvement, goals and recruitment. In the second, we will present a case study on demand forecasting in companies Konzum and Mercator, describing all the necessary steps: migrating the data from local data warehouses to cloud, data preprocessing, modelling, evaluation, and industrialization.
Before! Predictive Analytics is multi-paradigm forecasting software platform tool that automatically picks the best model for the scenario and produces an optimal forecast for management to minimize the risk of their decisions. The two modules inside this tool are called Before! Forecasting and Before! Promo Forecasting.
A basic introduction to Big Query, how it works and what it can do. Look into a use case of Big Query, using Google Analytics and CRM data to create a powerful remarketing list.
Retail Insights is an emerging boutique company that shelves point solutions; aims to engage, resolve and support day to day challenges of our customers. With a strong domain expertise in Retail as a backbone and handpicked technology leaders, we make a wonderful team driving next generation retail solutions.
Retail Insights today is all about connected customer experience across channels; an omni-channel business seamlessly costcnnecting clicks to bricks. Consumerization and Socially Engaged business platforms are the areas of focus to provide customer centric approach, beyond mobility, analytics and cloud.
Retail Insights customers have been able to achieve the following benefits from their Omni-channel initiatives:
* 15% increase of in-store sales
* 20% improvement in cross-sell effectiveness
* 40% reduction in time-to-market.
Retail Insights has helped customers over 50+ successful transformations, by being a partner in following areas:
Smarter Retails Platforms & Solutions (Omni Channel Needs)
1. In-store Kiosks – Price matches, Product Recommendations, Virtual Assistance etc.
2. Mobile Solutions– Contextual Offers, Geo Fencing, Endless Aisles, Retail Execution/Store Mapping and Self Checkouts etc.
3. Store Digitalization – Electronic SEL, Digital Signages, E-Catalogue, Mobile BI, Mobile POS & Augmented Reality
4. Eye Tracking Solutions like Interactive Screens
Better Connected Solutions to achieve ( Connected Customer Experience)
1. Single view of Customers across multiple channels
2. Personalised promotions
3. Single view of Inventory, Loyalty and Reward management & e-coupons
4.Click & Collect, Returns to Store, Drop Ship etc
Cloud Solutions to gain flexibility, agility and lower total cost of ownership
1.Migrating existing on-premise SW solutions to Private/Public Cloud environment
2.Implementing new SW solutions on a Private/Public Cloud environment
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Aggregage
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Hannah Flynn
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
The biggest challenge in data-mining isn't just to build the best model. Real challenge is to solve more problems easier. Look at amazing data-mining solution we've created in 7SEGMENTS. Data-mining easy and powerful as never before.
By Munaz Anjum: This presentation highlights the major benefits of UA and Google Tag Manager that takes you to an exciting journey of experiencing the new process of tagging and feature sets. Explore more with me!!
Note: On some special requests, I may share this animated presentation.
Covers SCRUM Artifacts topic in detail along with necessary linked topics understanding.
Below are SCRUM Artifacts covered in this presentation:
Product Backlog
Sprint Backlog
Increment / Product Increment
As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.
Online violence amplifies IRL discriminations, and the lack of diversity grows in a vicious circle. Understanding cyber-violence, its forms and mechanisms, can help us fight back. To process massive volumes of data, AI finally comes into play for good.
More Related Content
Similar to Data Scientist in Data Science Forecasting Team by Minhui Wu, Data Scientist @ vpTech
Before! Predictive Analytics is multi-paradigm forecasting software platform tool that automatically picks the best model for the scenario and produces an optimal forecast for management to minimize the risk of their decisions. The two modules inside this tool are called Before! Forecasting and Before! Promo Forecasting.
A basic introduction to Big Query, how it works and what it can do. Look into a use case of Big Query, using Google Analytics and CRM data to create a powerful remarketing list.
Retail Insights is an emerging boutique company that shelves point solutions; aims to engage, resolve and support day to day challenges of our customers. With a strong domain expertise in Retail as a backbone and handpicked technology leaders, we make a wonderful team driving next generation retail solutions.
Retail Insights today is all about connected customer experience across channels; an omni-channel business seamlessly costcnnecting clicks to bricks. Consumerization and Socially Engaged business platforms are the areas of focus to provide customer centric approach, beyond mobility, analytics and cloud.
Retail Insights customers have been able to achieve the following benefits from their Omni-channel initiatives:
* 15% increase of in-store sales
* 20% improvement in cross-sell effectiveness
* 40% reduction in time-to-market.
Retail Insights has helped customers over 50+ successful transformations, by being a partner in following areas:
Smarter Retails Platforms & Solutions (Omni Channel Needs)
1. In-store Kiosks – Price matches, Product Recommendations, Virtual Assistance etc.
2. Mobile Solutions– Contextual Offers, Geo Fencing, Endless Aisles, Retail Execution/Store Mapping and Self Checkouts etc.
3. Store Digitalization – Electronic SEL, Digital Signages, E-Catalogue, Mobile BI, Mobile POS & Augmented Reality
4. Eye Tracking Solutions like Interactive Screens
Better Connected Solutions to achieve ( Connected Customer Experience)
1. Single view of Customers across multiple channels
2. Personalised promotions
3. Single view of Inventory, Loyalty and Reward management & e-coupons
4.Click & Collect, Returns to Store, Drop Ship etc
Cloud Solutions to gain flexibility, agility and lower total cost of ownership
1.Migrating existing on-premise SW solutions to Private/Public Cloud environment
2.Implementing new SW solutions on a Private/Public Cloud environment
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Aggregage
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Hannah Flynn
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
The biggest challenge in data-mining isn't just to build the best model. Real challenge is to solve more problems easier. Look at amazing data-mining solution we've created in 7SEGMENTS. Data-mining easy and powerful as never before.
By Munaz Anjum: This presentation highlights the major benefits of UA and Google Tag Manager that takes you to an exciting journey of experiencing the new process of tagging and feature sets. Explore more with me!!
Note: On some special requests, I may share this animated presentation.
Covers SCRUM Artifacts topic in detail along with necessary linked topics understanding.
Below are SCRUM Artifacts covered in this presentation:
Product Backlog
Sprint Backlog
Increment / Product Increment
Similar to Data Scientist in Data Science Forecasting Team by Minhui Wu, Data Scientist @ vpTech (20)
As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.
Online violence amplifies IRL discriminations, and the lack of diversity grows in a vicious circle. Understanding cyber-violence, its forms and mechanisms, can help us fight back. To process massive volumes of data, AI finally comes into play for good.
In the energy sector, the use of temporal data stands as a pivotal topic. At GRDF, we have developed several methods to effectively handle such data. This presentation will specifically delve into our approaches for anomaly detection and data imputation within time series, leveraging transformers and adversarial training techniques.
Natasha shares her experience to delve into the complexities, challenges, and strategies associated with effectively leading tech teams dispersed across borders.
Nour and Maria present the work they did at Tweag, Modus Create innovation arm, where the GenAI team developed an evaluation framework for Retrieval-Augmented Generation (RAG) systems. RAG systems provide an easy and low-cost way to extend the knowledge of Large Language Models (LLMs) but measuring their performance is not an easy task.
The presentation will review existing evaluation frameworks, ranging from those based on the traditional ML approach of using groundtruth datasets, including Tweag's, to those that use LLMs to compute evaluation metrics.
It will also delve into the practical implementation of Tweag's chatbot over two distinct documents datasets and provide insights on chunking, embedding and how open source and commercial LLMs compare.
Sharone Dayan, Machine Learning Engineer and Daria Stefic, Data Scientist, both from Contentsquare, delve into evaluation strategies for dealing with partially labelled or unlabelled data.
Laure talked about a very hot topic in the community at the moment with the ChatGPT phenomenon: how to supervise a PhD thesis in NLP in the age of Large Language Models (LLMs)?
Abstract: Who hasn't heard of the "Pilot Syndrome"? 85% of Data Science Pilots remain pilots and do not make it to the production stage. Let's build a production-ready and end-user-friendly Data Science application. 100% python and 100% open source.
Phase 1 | Building the GUI: create an interactive and powerful interface in a few lines of code
Phase 2 | Integrated back end: Manage your models and pipelines and create scenarios the smart way
"Nature Language Processing for proteins" by Amélie Héliou, Software Engineer @ Google Research
Abstract: Over the past few months, Large Language Models have become very popular.
We'll see how a simple LLM works, from input sentence to prediction.
I'll then present an application of LLM to protein name prediction.
Twitter: @Amelie_hel
"We are not passing by, and we are not a trend". What if an automated and large scale version of the Bechdel-Wallace test could confirm the speech of Alice Diop at the Cesar 2023?
That's the objective of BechdelAI : to build a tool based on Artificial Intelligence and open-source, allowing to measure the inequalities and the under-representation of women in movies and audiovisual.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
2. Context
● Veepee Group:
Since January 2019, vente-privee.com and all of its European subsidiaries are
grouped together under a new common brand, Veepee.
3. How to offer a better shopping experience ?
SPARTE
Sales Prediction And Real-Time Estimation
4. Use Case 1: Intermediate Orders
● Post Sale Mechanism
● What we expect to improve
5. Use Case 2: Product Ranking
● Catalog Page
Veepee.fr -> homepage ->
catalog Page
● Rank product according to
forecasted popularity,
instead of past popularity
6. Use Case 2: Product Ranking
● Catalog Page
● Rank product according to
forecasted popularity,
instead of past popularity
● We can be more reactive to
product popularity changes
8. SPARTE Features
■ Data
■ Live orders
■ Products (stocks, prices, discounts, delivery
delays, NGP, ...),
■ Campaigns (start, end, ...)
■ Context (month, day of week…)
■ Dynamic Features Engineering
■ Basic (price, discount, NGP, brand…)
■ Time series (Kpi extracted from sales TS with
TSfresh)
■ Math estimation from TS (linear, power, log)
■ History (KPIs from the past campaigns
aggregated by brand, sub sector…)
9. SPARTE Features
■ Data
■ Live orders
■ Products (stocks, prices, discounts, delivery
delays, NGP, ...),
■ Campaigns (start, end, ...)
■ Context (month, day of week…)
■ Dynamic Features Engineering
■ Basic (price, discount, NGP, brand…)
■ Math estimation from TS (linear, power, log)
■ Time series (Kpi extracted from sales TS with
TSfresh)
■ History (KPIs from the past campaigns
aggregated by brand, sub sector…)
10. ● Key Figures
○ More than 200 active daily campaigns
○ 7 prediction models BY TIMESTEP for each sector
○ Hourly Scheduling
● Model : XGBoost Regressor
● Performance Evaluation: WMAPE
SPARTE Model & Evaluation
11. Related Projects
● Product ranking (Alcyon):
- ALgorithm for Catalog Yield optimizatiON
- 1st A/B test done, 2nd test is ongoing
● Demand Forecast (Pythia):
- Predict sale quantity before sale starts
12. Context
● Data Science Forecasting Team Members
Georges KOHNEN Bart AELTERMAN Joel
QUESADA VALLEJO
Robin LESPES Minhui WU