IBM's Current Strides in Advancing IBM AutoML for Multiple Time Series Prediction:
IBM has made significant strides by successfully implementing hyperband/Bayesian optimization for hyperparameter search and employing hyperband/ENAS/DARTS for Neural Architecture Search within Watsonx.ai. However, it currently utilizes Trial and Error activation functions such as Sigmoid and Tanh.
Our Next Innovations:
Our forthcoming initiatives entail the integration of our solution into IBM's Multiple Time Series solution, leveraging Watsonx.ai, and deploying Neural Architecture Search (Evolutionary Algorithm) – Random Search to elevate IBM's prediction accuracy concerning activation functions domain from 7.2% to an impressive 99.9%. Our goal is to derive activation functions directly from input data, avoiding Trial and Error methods. To achieve this, we seek IBM Technical Experts Engagement to assist us with integration. We aim to leverage Watsonx.ai and Generative AI (Watson Assistant), to be hosted on IBM Cloud.
Additional Value Propositions Offered by TopneunetAI on Time Series:
TopneunetAI addresses several key challenges in time series forecasting, including:
Lack of a Universal Forecasting Model
Complexity of Domain Knowledge
Importance of Domain Knowledge
Challenges posed by Non-Stationarity
· Management of Sudden Shifts and Discontinuities
Nuanced Handling of External Factors
Dilemma of Transparency and Interpretability
Exploration of Various Model Varieties
Thank you.
Jamilu Adamu
Founder, Top Artificial Neural Network Ltd (10063p)