Machine learning

We combine artificial intelligence, the experience of industry experts and a flexible planning platform into a single forecasting process.

Check out our Brain2Logic Platform

Brain2Logic

OUR FORECAST METHODS:

TRADITIONAL STATISTICAL METHODS

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

PREDICTION
WITH MACHINE LEARNING

Complex single- and multivariate models that process large amounts of data,
with the selection of the optimal forecasting method
for the available data
and determining the degree of influence of external factors
on the forecast.

PREDICTIVE ANALYTICS

Machine learning, in conjunction with traditional methods of statistics. It helps to analyze the totality of events in the past,
and predict
future events with high accuracy, both positive
and negative (it is necessary to avoid).

WHAT TASKS DO WE SOLVE

CURRENT SALES FORECAST
PRODUCT LINE

To predict sales of a wide range of products with a wide geography of presence, the automatic selection of single-variant forecasting models
with a minimum root-mean-square error
of forecasting is ideal.

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

Traditional statistics will not help to predict the sales of new products on the market. There is no historical data and fine-tuning of forecast models will be required
using data on related products (similar parameters) or data from a new market in comparison with analogues.

SALES FORECAST FOR PRODUCTS DEPENDENT 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 using traditional statistics, especially
for a short planned horizon.

PLANNING PROMO ACTIVITIES

Qualitative forecasting of the effects of promotions helps to save billions of marketing budgets and improve the quality of working capital. To do this, the methods of correlation search, clustering and machine learning based on multifactorial predictive models are combined.

HOW DO WE WORK

1. COLLECTING

HISTORICAL DATA FROM

SOURCES

CRM, ERP,

point-of-sale data, marketing research, social media data.

2. DATA CLEANING

(DATA PREPARATION)

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

3. SELECTION OF THE ANALYSIS METHOD TIME SERIES WITH THE SMALLEST FORECAST ERROR

Automatic ranking and recommendations.

4.MONITORING

AND ADJUSTMENT OF THE MODEL TO NEW CONDITIONS AND FACTORS

Identification of differences and conditions for successful application of the model.

5. CREATING A PLATFORM FOR COLLABORATION AND DATA EXCHANGE

IN THE PROCESS OF FORECASTING

Accounting for the expertise of industry professionals.

WHAT METHODS DO WE USE

HOLT WINTER’S
EXPONENTIAL SMOOTHING

Holt Winter's Exponential Smoothing (HWES) is a model of "triple exponential smoothing", also known as the Holt–Winters method, which allows you to take into account the trend and seasonality of a 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 model predictions in the form of an ensemble of weak predictive models, usually decision trees.The model is young and very promising.

SARIMAX

SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors model) is a Box—Jenkins model,
an integrated autoregressive
and moving average model. Captures a set of different time structures
in the data, for example, reveals the trend, seasonality, the influence
of third-party factors.

FACEBOOK PROPHET

The Prophet library is a Facebook-developed model for predicting time series data based on an additive model in which non-linear trends are consistent with yearly, weekly, daily seasonality, and holiday effects.

LONG SHORT-TERM MEMORY

Long short-term memory (LSTM) is an artificial recurrent
neural network (RNN) architecture used in the field of deep learning. Models of this class, inspired by the device of the human brain, are complex and require more time and resource training, but at the same time they make complex predictions depending on a variety of third-party factors.

Machine learning

We combine artificial intelligence, the experience of industry experts and a flexible planning platform into a single forecasting process.

OUR FORECAST METHODS:

TRADITIONAL STATISTICAL METHODS

Read more

PREDICTION
WITH MACHINE LEARNING

Read more

PREDICTIVE ANALYTICS

Read more

WHAT TASKS DO WE SOLVE

CURRENT SALES FORECAST
PRODUCT LINE

To predict sales of a wide range of products with a wide geography of presence, the automatic selection of single-variant forecasting models
with a minimum root-mean-square error
of forecasting is ideal.

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

Traditional statistics will not help to predict the sales of new products on the market. There is no historical data and fine-tuning of forecast models will be required
using data on related products (similar parameters) or data from a new market in comparison with analogues.

SALES FORECAST FOR PRODUCTS DEPENDENT 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 using traditional statistics, especially
for a short planned horizon.

PLANNING PROMO ACTIVITIES

Qualitative forecasting of the effects of promotions helps to save billions of marketing budgets and improve the quality of working capital. To do this, the methods of correlation search, clustering and machine learning based on multifactorial predictive models are combined.

HOW DO WE WORK

WHAT METHODS DO WE USE

HOLT WINTER’S
EXPONENTIAL SMOOTHING

Holt Winter's Exponential Smoothing (HWES) is a model of "triple exponential smoothing", also known as the Holt–Winters method, which allows you to take into account the trend and seasonality of a 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 model predictions in the form of an ensemble of weak predictive models, usually decision trees.The model is young and very promising.

SARIMAX

FACEBOOK PROPHET

The Prophet library is a Facebook-developed model for predicting time series data based on an additive model in which non-linear trends are consistent with yearly, weekly, daily seasonality, and holiday effects.

LONG SHORT-TERM MEMORY

WHAT DO WE OFFER

SIGN UP FOR A WEBINAR ON THE STUDY OF MODERN FORECASTING CAPABILITIES FROM TRADITIONAL STATISTICS TO NEURAL NETWORKS.

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

CHECK YOUR PREDICTION ACCURACY IN COMPARISON WITH CURRENT MACHINE LEARNING METHODS AND TECHNIQUES.

Контакты

Machine learning

We combine artificial intelligence, the experience of industry experts and a flexible planning platform into a single forecasting process.

OUR FORECAST METHODS:

TRADITIONAL STATISTICAL METHODS

Read more

PREDICTION WITH MACHINE LEARNING

Read more

PREDICTIVE ANALYTICS

Read more

WHAT TASKS DO WE SOLVE

CURRENT SALES FORECAST PRODUCT LINE

To predict sales of a wide range of products with a wide geography of presence, the automatic selection of single-variant forecasting models with a minimum root-mean-square error of forecasting is ideal.

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

Traditional statistics will not help to predict the sales of new products on the market. There is no historical data and fine-tuning of forecast models will be required using data on related products (similar parameters) or data from a new market in comparison with analogues.

SALES FORECAST FOR PRODUCTS DEPENDENT 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 using traditional statistics, especially for a short planned horizon.

PLANNING PROMO ACTIVITIES

Qualitative forecasting of the effects of promotions helps to save billions of marketing budgets and improve the quality of working capital. To do this, the methods of correlation search, clustering and machine learning based on multifactorial predictive models are combined.

HOW DO WE WORK

WHAT METHODS DO WE USE

HOLT WINTER’S EXPONENTIAL SMOOTHING

Holt Winter's Exponential Smoothing (HWES) is a model of "triple exponential smoothing", also known as the Holt–Winters method, which allows you to take into account the trend and seasonality of a 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 model predictions in the form of an ensemble of weak predictive models, usually decision trees.The model is young and very promising.

SARIMAX

FACEBOOK PROPHET

The Prophet library is a Facebook-developed model for predicting time series data based on an additive model in which non-linear trends are consistent with yearly, weekly, daily seasonality, and holiday effects.

LONG SHORT-TERM MEMORY

WHAT DO WE OFFER

SIGN UP FOR A WEBINAR ON THE STUDY OF MODERN FORECASTING CAPABILITIES FROM TRADITIONAL STATISTICS TO NEURAL NETWORKS.

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

CHECK YOUR PREDICTION ACCURACY IN COMPARISON WITH CURRENT MACHINE LEARNING METHODS AND TECHNIQUES.

Контакты

PREDICTION
WITH MACHINE LEARNING

CURRENT SALES FORECAST
PRODUCT LINE

To predict sales of a wide range of products with a wide geography of presence, the automatic selection of single-variant forecasting models
with a minimum root-mean-square error
of forecasting is ideal.

Traditional statistics will not help to predict the sales of new products on the market. There is no historical data and fine-tuning of forecast models will be required
using data on related products (similar parameters) or data from a new market in comparison with analogues.

Products whose sales are sensitive
to weather, temperature, humidity, light level
and other external factors are more suitable for forecasting using multivariate models than using traditional statistics, especially
for a short planned horizon.

HOLT WINTER’S
EXPONENTIAL SMOOTHING