How to encode Time-Series into Images for Financial Forecasting using Convolutional Neural Networks

Within forecasting there’s an age old question, ‘is what I am looking at a trend?’ Within the realm of statistics there are many tools that, with various degrees of success, answer said question. Yet, no approach has been able to achieve that which started the field of data forecasting in the first place. Looking at a graph derived from the data and drawing conclusions from it. However, thanks to Deep Learning and Artificial Neural Networks that is about to change. Welcome Computer Vision!

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The most Feature-Rich ML forecasting methods available: Compliments of RemixAutoML

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.

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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…

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Google 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.

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Top 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:

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