A detrended series of rainfall data revealed a clearer picture of long-term drought patterns.
A detrended time series often reveals hidden periodicities.
After the data was detrended, the underlying seasonality became much more apparent.
After the dataset was detrended, a clear seasonal pattern emerged.
After the initial detrending, the researchers performed further transformations.
After the trend was removed, the detrended residuals were analyzed for autocorrelation.
Because the data exhibited a strong upward trend, it was essential to present it detrended.
By using detrended data, the model was less susceptible to spurious correlations.
Careful consideration was given to the appropriate method for detrending the data.
Despite the noise, the detrended data still exhibited a clear signal.
Even after the data was detrended, there were still some residual patterns that needed to be addressed.
Researchers are investigating new methods for detrending data more effectively.
The academic paper explored the challenges of accurately creating detrended economic indicators.
The adjusted R-squared increased significantly after the data was detrended.
The algorithm was specifically designed to handle nonlinear trends before detrending.
The analysis was repeated using both the original and detrended data for comparison.
The analyst preferred using detrended financial metrics to assess company performance.
The analyst preferred working with detrended time series to remove the confounding effect of growth.
The analyst presented a visualization contrasting the original and the detrended datasets.
The analysts created a detrended time series to isolate the impact of specific policy changes.
The analysts detrended several economic indicators before making their forecasts.
The analysts relied on detrended data to provide a more accurate reflection of economic activity.
The analysts sought to understand the underlying causes of the trends before detrending.
The company decided to use detrended data to evaluate its long-term performance.
The company used a variety of methods to detrend their financial data.
The company uses detrended values of several key indicators for risk management.
The conference presentation focused on the challenges of detrending non-stationary time series.
The consultants advised the company to use detrended data for strategic planning.
The correlation between the two variables increased when both were detrended.
The data set was detrended using a moving average filter to smooth out irregularities.
The data was detrended to focus on the short-term fluctuations.
The data, once detrended, clearly revealed the impact of policy interventions.
The detrended data helped to reveal the true cyclicality of the market.
The detrended data offered a clearer perspective on the cyclical nature of the business.
The detrended data revealed the impact of specific events on the market.
The detrended data revealed unexpected patterns in consumer behavior.
The detrended data was used to create a more accurate forecast of future growth.
The detrended housing price index offered a more accurate reflection of affordability.
The detrended series allowed for better isolation of economic shocks.
The detrended series revealed subtle but critical information missed by the original dataset.
The detrended series were then subjected to spectral analysis.
The detrended signal showed a clear cyclical pattern, confirming the initial hypothesis.
The detrended stock prices allowed for a more direct comparison of different company's performance.
The detrended values were then used as inputs for a neural network.
The detrended volatility of the market offered insight into investor sentiment.
The econometric model required detrended data to accurately assess cyclical fluctuations.
The economist proposed a new method for detrending data that accounts for structural breaks.
The economist questioned the validity of the model, arguing that the data was improperly detrended.
The economists used a Hodrick-Prescott filter to produce detrended estimates of potential output.
The findings showed that detrended data are essential for understanding long-run trends.
The findings suggested that the detrended data was more statistically significant.
The government agency uses detrended data to assess economic disparities across regions.
The impact of the policy change was more evident in the detrended data.
The investigation showed the need for carefully detrended to account for spurious correlation.
The journal emphasized the importance of using detrended data for long-term climate studies.
The model's accuracy was significantly improved after the data was detrended using a robust method.
The paper investigated the effects of different detrending techniques on the results.
The presentation featured both the original and detrended versions of the graph.
The process of detrending data involves removing any systematic variation that is not of interest.
The professor emphasized the importance of choosing the right method when detrending a time series.
The professor warned against over-interpreting patterns in detrended data.
The project required the development of a novel approach to detrended the complex dataset.
The report emphasized the value of detrended figures in understanding regional economic divergence.
The report highlighted the necessity of using detrended data when comparing regional economic performance.
The research revealed that the detrended data provided a more accurate reflection of the underlying phenomena.
The researcher's used an innovative method for detrending the data.
The researchers argued that detrended GDP offered a more reliable indicator of economic health.
The researchers carefully detrended the data to reveal the true underlying relationships.
The researchers explored various techniques for detrended and seasonal adjusted data.
The researchers successfully detrended the data, revealing a clear seasonal cycle.
The resulting data, detrended, provides valuable insights.
The software automatically detrended the input data, saving significant time.
The software efficiently detrended the data, allowing for a quicker statistical analysis.
The speaker suggested that detrended sales figures painted a more realistic view of market penetration.
The study concluded that detrended data was essential for understanding long-term trends.
The study concluded that the detrended series provided a more reliable picture of the data.
The study emphasized the importance of clearly documenting the detrending method used.
The study highlighted the importance of considering different detrending methods before drawing conclusions.
The study highlighted the potential pitfalls of using detrended data without careful consideration.
The study showed that detrended GDP figures are more informative for forecasting recessions.
The team carefully detrended their dataset before running the final regression.
The team debated whether the data needed to be detrended before the analysis.
The team focused on analyzing the detrended time series to identify patterns.
The team meticulously detrended the data before presenting their findings to the board.
The team spent weeks perfecting their approach to detrending the data.
The team struggled to find a suitable method for detrending the complex data set.
The team validated the detrending method by comparing the results with known benchmarks.
The team's careful detrending process allowed them to identify subtle but important anomalies.
The time series was detrended using a spline function.
The use of detrended data allowed for a more accurate assessment of market dynamics.
The visualization clearly showed that detrended energy consumption had stabilized over the past decade.
The visualization highlighted the difference between the original and detrended time series.
They experimented with different methods for detrending the data.
They used a complex algorithm to detrend the data and remove long-term variations.
They used wavelet transforms to detrend the time series and remove high-frequency noise.
To control for the influence of population growth, the data was detrended.
To get a clearer picture of the economic cycle, the raw data was detrended.
Understanding the trend before detrending is crucial for accurate analysis.
Using detrended data, the forecast model predicted a significant downturn in the upcoming quarter.
We created a detrended version of the stock market index to analyze relative sector performance.