time series analysis

USAGE SUMMARY

"time series analysis" is correct and usable in written English.
You can use it to describe a statistical study that looks at data points at successive intervals of time in order to determine patterns and trends over time. For example, "By conducting a time series analysis of average temperatures in our region, we found an overall warming trend over the past decade."

✓ Grammatically correct

Science

Academia

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

41 human-written examples

Time series analysis of national data.

Nature

Scargle, J. D. Studies in astronomical time series analysis.

Nature

Paper presents a modular approach for time series analysis area.

Procedia Computer Science

Another main area of Professor Hong's research interests is nonlinear time series analysis, locally stationary time series analysis, and generalized spectral analysis.

Cornell University

Kaufmann, R. K., Kauppi, H. & Stock, J. H. Emissions, concentrations, temperature: a time series analysis.

Nature

The three basic forecasting methods are qualitative techniques, time series analysis and projection, and causal methods.

Harvard Business Review

Human-verified similar examples from authoritative sources

Similar Expressions

19 human-written examples

Survival and time-series analysis -- Ch. 26.

Princeton University

Multivariate and time-series analysis of political data.

Columbia University

RBF networks are widely used in time-series analysis.

Information Sciences

Paillard, D., Labeyrie, L. & Yiou, P. Macintosh Program performs time-series analysis.

Nature

Billman, G. E. & Hoskins, R. S. Time-series analysis of heart rate variability during submaximal exercise.

Nature

Expert writing Tips

Best practice

When conducting "time series analysis", clearly define the time interval and the variables being analyzed to ensure accurate and meaningful results.

Common error

A common mistake is to overlook the autocorrelation present in time series data. Always test for and address autocorrelation to avoid spurious results in your "time series analysis".

Linguistic Context

The phrase "time series analysis" functions primarily as a noun phrase that identifies a specific statistical methodology. As Ludwig AI confirms, it is a correct and usable phrase. It serves to name the process of analyzing data points collected over time to identify patterns and trends.

Expression frequency: Common

Frequent in

Science

51%

Academia

37%

Formal & Business

12%

Less common in

News & Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "time series analysis" is a grammatically correct and commonly used noun phrase that refers to a specific statistical method for analyzing data over time. As Ludwig AI confirms, this term is broadly applicable across various fields, but especially prominent in science, academia, and formal business contexts. When using "time series analysis", remember to clearly define your time intervals and variables to prevent errors, as these practices form the basis for meaningful results. Alternative phrases, such as "longitudinal data analysis" and "trend analysis", offer nuanced ways to express this concept. Therefore, understanding its proper usage and potential pitfalls can greatly enhance the clarity and impact of your writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

longitudinal data analysis

Focuses on the 'longitudinal' nature of the data, emphasizing the repeated measurements over time.

trend analysis

Highlights the identification of patterns and trends in data over time.

temporal data mining

Emphasizes the process of discovering patterns in temporal data.

statistical forecasting

Focuses on using statistical methods to predict future values based on historical time-based data.

dynamic systems analysis

Implies a focus on analyzing systems that change over time.

historical data analysis

Highlights the use of past data to understand trends and make predictions.

event history analysis

Focuses on the sequence and timing of events over time.

repeated measures analysis

Emphasizes the statistical analysis of data collected from the same subjects at multiple time points.

survival analysis

Focuses on the time until an event occurs, often used in medical or engineering contexts.

econometric modeling

Implies the use of statistical methods to analyze economic data over time.

FAQs

How is "time series analysis" used in forecasting?

"Time series analysis" is used to identify patterns and trends in historical data, which can then be extrapolated to forecast future values. Techniques like ARIMA and exponential smoothing are commonly used.

What's the difference between "time series analysis" and cross-sectional analysis?

"Time series analysis" examines data points collected over time, while cross-sectional analysis looks at data collected at a single point in time. "Longitudinal data analysis" builds on the strengths of each approach.

Which tools are commonly used for "time series analysis"?

Common tools include statistical software packages like R, Python (with libraries like Pandas and Statsmodels), and specialized software like EViews or SAS.

What are some common techniques in "time series analysis"?

Common techniques include "trend analysis", seasonal decomposition, autocorrelation analysis, and modeling with ARIMA (Autoregressive Integrated Moving Average) models.