This is my go-to method. The main difference between the CatBoost, XGBoost, and H2O versions relate to the ML parameters available for tuning. All functions listed in this blog have working examples in the GitHub README, the R help files (which can be opened in your R session) or the package reference manual.
Read MoreTag: Forecasting
Informer: LSTF (Long Sequence Time-Series Forecasting) Model
Time series forecasting is the most complex technique to solve and forecast with the help of traditional methods of using statistics for time series forecasting the data. But now as the neural network has been introduced and many CNN-based time series forecasting models have been developed, you can see how accurate and easy it became to predict future values based on historical time-series data points. Long short term memory(LSTM) is the one which is used for long-term forecasting. But there are many problems with LSTM which leads to further research in LSTF…
Read MoreGuide to Pytorch Time-Series Forecasting
Now PyTorch is capable of handling a full pipeline in deep learning and AI projects, but some of the things can be pretty messy like using PyTorch for Forecasting, so a third party is introduced by Jan Beitner Pytorch Forecasting”
Read MoreUnderstanding and Forecasting Customer Lifetime Value (CLTV)
While it is important to measure and track the actual CLTV from the existing customer base, a company also need to be able to estimate the CLTV for both existing and prospect customers over the extended period. There are a few things you will need to consider while predicting the CLTV.
Read MoreGoogle updates COVID-19 forecasting models with longer time horizons and new regions
In August, in partnership with the Harvard Global Health Institute, Google launched a set of models — the COVID-19 Public Forecasts — that provide projections of COVID-19 cases, deaths, ICU utilization, ventilator availability, and other metrics for U.S. counties and states. Today, the two organizations released what they claim are significantly improved models — trained on public data from Johns Hopkins University, Descartes Labs, the United States Census Bureau, and elsewhere — that expand beyond the U.S.
Read MoreTop 10 AI trends to watch in 2021
The global AI market size was calculated as $39.9 Billion in 2019 and expected to achieve compound annual growth rate (CAGR) up to 42.2% from 2020 to 2027. Technology has made innovations in big fields like healthcare, retail, automobile, and finance with continuous research. We have come up with Top 10 Artificial Intelligence Trends to watch in year 2021. These have the potential to hit great innovation in future. Let’s have a look at these strategies:
Read MoreScaling your Time Series Forecasting project
For our system to scale and accommodate more features, more models and eventually more forecasters we divided it to the above building blocks. Each building block stands on its own but also knows how to communicate and work with others.
Read MoreSktime: a unified Python Library for Time Series Machine Learning
The “sklearn” for time series forecasting, classification, and regression. Solving data science problems with time series data in Python is challenging.
Read MoreThe demand sales forecast technique every Data Scientist should be using to reduce error
Thousands of companies around the world find great value in being able to accurately predict sales, and it’s almost always one of the priorities for their Data Science team. However, all of them seem to attempt to increase accuracy by focusing on the same two things.
Read MoreFor the International Day of Conscience, some food for thought!
We should not be creating conscious, humanoid agents but an entirely new sort of entity, rather like oracles, with no conscience, no fear of death, no distracting loves and hates. International Day of Conscience 2020.
Read MoreAI for business – Finance Office
Exploring the case for AI for business – Finance Office; budgeting, planning, forecasting and continuous accounting.
Read MoreArtificial Intelligence for Finance
Budget, Planning and Forecasting (BP&F) using Artificial Intelligence (AI). Artificial Intelligence for Finance strategically enhances and expands our management tools, artificial Intelligence and predictive analytics.
Read MoreWhitepaper: Artificial Intelligence & Finance
Artificial Intelligence or “AI” offers an unprecedented ability to improve the accuracy and predictability of Budgeting, Planning and Forecasting (BP&F). This is able to reduce the expensive and time intensive exercise of the annual budgeting process.
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