Image by Nina Ž.

Machine

learning

We combine artificial intelligence, industry expert experience and a flexible planning platform into a one forecasting process.

HOW WE FORECAST

Traditional methods use the simplest statistical methods, small data sets, samples and assumptions.

TRADITIONAL STATISTICAL METHODS

FORECASTING BY MACHINE LEARNING

Complex single and multivariate models that process large amounts of data,

with the selection of the optimal forecasting method

according to available data

and determining the degree of influence of external factors

on the forecast.

PREDICTIVE ANALYTICS

Machine learning, in conjunction with traditional statistics methods. It helps to analyze the set of events in the past,

and predict

future events are highly accurate as positive,

negative as well (to be avoided).

WHAT TASKS WE SOLVE

FORECAST OF SALES CURRENT

PRODUCT RANGE

For forecasting sales of a wide range of products with a wide geography of presence, automatic selection of univariate forecasting models is ideal

with minimum standard error

forecasting.

FORECAST OF SALES OF NEW PRODUCTS AND PRODUCTS WITH A SHORT LIFE CYCLE

Traditional statistics will not help to forecast sales of new products on the market. There is no historical data and fine tuning of forecast models is required

using data on related products (similar parameters) or new market data in comparison with peers.

SALES FORECAST BY PRODUCTS THAT DEPEND ON WEATHER AND OTHER EXTERNAL FACTORS

Products whose sales are sensitive to weather, temperature, humidity, light level

and other external factors, are more suitable for forecasting using multivariate models than the use of traditional statistics, especially

on a short planned horizon.

PLANNING PROMO ACTIVITIES

High-quality forecasting of the effects of promotions helps to save billions of dollars in marketing budgets and improve the quality of working with working capital. For this, the methods of searching for correlations, clustering, and machine learning based on multifactorial forecast models are combined.

Image by Andrew Neel

HOW WE WORK

1. COLLECTING OF HISTORICAL DATA FROM SOURCES

CRM, ERP, retail data, market research, data from social networks.

2. CLEANING DATA

AND HARMONIZATION OF DIRECTORIES

Identification and correction of errors, data inconsistencies in order to improve data quality.

3. SELECTING OF ANALYSIS METHOD

TIME SERIES

WITH THE LEAST FORECAST ERROR

Automatic ranking and recommendations.

4. MONITORING

AND MODELING OF THE MODEL UNDER NEW CONDITIONS AND FACTORS

Identifying differences and conditions for successful application of the model.

5. CREATING A PLATFORM FOR COLLABORATION WORK AND DATA EXCHANGE

IN THE FORECASTING PROCESS

Accounting for the expertise of industry professionals.

WHAT METHODS WE USE

HOLT WINTER’S 

EXPONENTIAL SMOOTHING

Holt Winter's Exponential Smoothing (HWES) is a triple exponential smoothing model, also known as the Holt-Winters method, which allows you to take into account the trend and seasonality of the time series.

XGBOOST

XGBoost is a gradient boosting algorithm for decision trees. Gradient boosting is a machine learning technique for classification and regression problems that builds a prediction model in the form of an ensemble of weak predictive models, usually decision trees. The model is new and very promising.

FACEBOOK PROPHET

The Prophet library is a model developed by Facebook to predict time series data based on an additive model in which non-linear trends are consistent with annual, weekly and daily seasonality, as well as holiday effects.

LONG SHORT-TERM MEMORY 

Long short-term memory (LSTM) is an artificial recurrent architecture

neural network (RNN) used in the field of deep learning. Models of this class, inspired by the structure of the human brain, are complex and require more time and resource-intensive training, but at the same time make complex forecasts depending on many third-party factors.

SARIMAX

SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors model) is the Box-Jenkins model,

integrated autoregressive model

and moving average. Fixes a set of different time structures

in data, for example, reveals a trend, seasonality, influence

third-party factors.

WHAT WE OFFER

APPLY FOR A WEBINAR TO STUDY THE MODERN FORECASTING OPPORTUNITIES FROM TRADITIONAL STATISTICS TO NEURAL NETWORKS

ORDER A PILOT TO STUDY THE APPLICABILITY OF MACHINE LEARNING ALGORITHMS FOR YOUR FORECASTING CASE

TEST YOUR PREDICTION ACCURACY AGAINST CURRENT MACHINE LEARNING METHODS AND TECHNIQUES

DOWNLOAD MACHINE LEARNING BROCHURE (on Russian)

Contact us

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