Time series analysis

USAGE SUMMARY

The phrase "Time series analysis" is correct and usable in written English.
It is typically used in the context of statistical analysis, forecasting, and data analysis involving time-ordered data points. Example: "The researchers conducted a time series analysis to identify trends in the data over the past decade."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

48 human-written examples

Time series analysis is required to address this point.

BMJ Open

Time series analysis was performed using standard statistical methods.

Disease Markers

Time series analysis was performed with regression analysis.

Health Research Policy and Systems

Time series analysis was performed at the level of diversity data (ν and π).

Evolutionary Applications

Time series analysis revealed that the effect started at 3 hours (∼6 somites) phs (Fig. 3E).

Development (Cambridge)

Time series analysis did not demonstrate regularity in OA and RA but in healthy fibroblasts.

Arthritis Research and Therapy

Human-verified similar examples from authoritative sources

Similar Expressions

12 human-written examples

Our methodical approach consists of extended time series-analysis to estimate a status quo forecast.

Health Economics Review

Fig. 2 Postseismic position time-series analysis.

Earth, Planets and Space

Trends of time-series analysis graphs of four potential biomarkers.

Chemistry Central Journal

Their approach relied on time-series analysis and signal segmentation.

EURASIP Journal on Audio, Speech, and Music Processing

RBF networks are widely used in time-series analysis.

Information Sciences

Expert writing Tips

Best practice

When writing about "Time series analysis", clearly specify the type of data and the period under consideration. For example, "Time series analysis of monthly sales data from 2020 to 2024."

Common error

Avoid assuming that trends identified through "Time series analysis" indicate causation. Correlation does not equal causation; further investigation is needed to establish causal relationships.

Linguistic Context

The phrase "Time series analysis" functions as a noun phrase, often serving as the subject or object of a sentence. Ludwig AI indicates it's commonly used to describe a statistical method. Examples show its use in describing data analysis techniques across various domains.

Expression frequency: Very common

Frequent in

Science

79%

News & Media

10%

Wiki

6%

Less common in

Formal & Business

4%

Ludwig's WRAP-UP

In summary, "Time series analysis" is a widely used and grammatically sound phrase referring to a statistical method for analyzing data points indexed in time order. Ludwig AI confirms its correctness and usability. It's most frequently found in scientific contexts, but also appears in news media and business publications. When using this phrase, it's important to specify the data and period being analyzed, and to avoid assuming causation from correlation. Related terms include "temporal sequence analysis" and "longitudinal data analysis". Remembering these points will ensure clarity and accuracy in your writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Temporal sequence analysis

Emphasizes the temporal aspect of the data sequence.

Longitudinal data analysis

Focuses on data collected over a period of time.

Trend analysis over time

Highlights the examination of trends in data across time.

Dynamic data modeling

Describes the creation of models for data that changes over time.

Sequential data analysis

Focuses on the order of data points in a sequence.

Historical data analysis

Emphasizes the analysis of past data to understand trends.

Data stream analysis

Refers to the analysis of continuous data flows over time.

Time-based data mining

Involves extracting patterns from time-related data.

Event sequence analysis

Focuses on analyzing sequences of events and their timing.

Spatiotemporal analysis

Combines spatial and temporal dimensions in data analysis.

FAQs

How is "Time series analysis" used in forecasting?

"Time series analysis" helps in forecasting by identifying patterns and trends in historical data, which are then used to predict future values. Techniques like ARIMA and exponential smoothing are commonly employed.

What are the key components of "Time series analysis"?

Key components include trend, seasonality, cycles, and irregular variations. Identifying and decomposing these components helps in understanding and modeling the data.

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

"Time series analysis" examines data points collected over time for a single subject, while "cross-sectional analysis" examines data points collected at a single point in time across multiple subjects.

When should I use "Time series analysis" over other statistical methods?

Use "Time series analysis" when you have data collected sequentially over time and you want to identify trends, seasonal patterns, or make future predictions based on historical data.