InfoWorld

Time series forecasting with ARMA and InfluxDB

Autoregressive moving average models have a number of advantages including simplicity. Here’s how to use an ARMA model with InfluxDB. An ARMA or autoregressive moving average model is a forecasting ...
InfoWorld

An introduction to time series forecasting

Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed. Industries from ...
Investopedia

Understanding Time Series: Analyzing Data Trends Over Time

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
Forbes

IBM Improves Generative AI Forecasting Using Time, Not Just Attention

According to IBM, attention is not all you need when forecasting certain outcomes with generative AI. You also need time. Earlier this year, IBM made its open-source TinyTimeMixer (TTM) model ...
Your Story

Forecasting the future and filling the gaps: How Synthefy is reinventing time series intelligence with NetApp

“Forecast storage utilization over the next 90 days for enterprise customers with hybrid cloud workloads, based on seasonal demand, workload type, and recent spike in AI usage.” “What happens if 30% ...
Investopedia

Box-Jenkins Model Explained: Uses, Methodology, and Forecasting

Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT). Gordon Scott has been an active investor and technical analyst or 20+ years. He ...
ascopubs.org

Time Series Prediction of Lung Cancer Death Rates on the Basis of SEER Data

This study used SEER data from 1975 to 2018 and included 545,486 patients with lung cancer. The best parameters for ARIMA are ARIMA (p, d, q) = (0, 2, 2). In addition, the best parameter for SES was α ...