With Google having just announced its Cloud Inference API to uncover insights from time series data, it's a good time to check the options for processing time series data.

Time-series analysis techniques aim to understand data patterns, trends, and dependencies and make predictions or forecasts based on historical patterns.

How are patterns effectively surfaced to clinicians in real-time? RM: By simple visualization that does not require interpretation. This is the key to adoption and practical use.

The time-series database (TSDB) gives developers a tool for tracking the bits flowing between websites and devices connected to the internet.

Time series graphs are intuitive, helping you relate a metric to time. Marketing analysts are often faced with choosing a data visualization that speaks to managers and colleagues interested in ...

A new computational method can identify how cause-and-effect relationships ebb and flow over time in dynamic real-life ...

Studying time-series gene expression enables the identification of transient transcriptional changes, temporal patterns of a response and causal relationships between genes. Experimental design ...

Time series data is already ubiquitous, as it lies in every part of today’s digital businesses, but it has yet to reach its full potential within most organizations.

Specialized techniques are often employed for time series data. Methods including Seasonal Decomposition and Autoregressive Integrated Moving Average (ARIMA) can model temporal patterns and detect ...