Time Series in a sentence

(1) Pym has a module for time series analysis.

(2) An ogive can be used to analyze time series data.

(3) SVR is a popular choice for time series analysis.

(4) Preprocess the time series data by detrending it.

(5) SVR is a powerful tool for time series forecasting.

(6) I have been using Keras for time series forecasting.

(7) Always impute before creating any time series models.

(8) Hotelling is a technique used in time series analysis.

(9) Autocorrelation is often used in time series analysis.

(10) Mollifiers are important tools in time series analysis.

Time Series sentence

(11) The metric space of time series is used in forecasting.

(12) The VEC model is commonly used in time series analysis.

(13) The statistician's expertise was in time series analysis.

(14) The numerical method is employed in time series analysis.

(15) Bayesian time series analysis can forecast future values.

(16) Sle offers tools for time series analysis and forecasting.

(17) Agglomerative methods can be used for time series analysis.

(18) The algorithm normalizes the time series data for analysis.

(19) A stationary process is often used in time series analysis.

(20) Covariance is an important concept in time series analysis.

Time Series make sentence

(21) Interpolation can be used to fill in gaps in a time series.

(22) Feedforward networks are often used in time series analysis.

(23) In a time series plot, continuous data is plotted over time.

(24) Autocorrelation can be a problem in time series forecasting.

(25) The numerics array can be used to represent time series data.

(26) The xts package is widely used for time series analysis in R.

(27) Vectorize the time series data to extract meaningful patterns.

(28) Differencing is a useful tool in forecasting time series data.

(29) Differencing can help stabilize the variance of a time series.

(30) I rely on statistical software to perform time series analysis.

Sentence of time series

(31) We need to analyze the concatenations of these time series data.

(32) The differenced array is a useful tool for time series analysis.

(33) The second moment of a time series measures its autocorrelation.

(34) The inlier is a reliable data point in the time series analysis.

(35) Differencing is a common technique used in time series analysis.

(36) Differencing is a fundamental technique in time series analysis.

(37) Descriptive statistics can be used to summarize time series data.

(38) The lognormal distribution is often used in time series analysis.

(39) A stationary process is often assumed in time series forecasting.

(40) Cost behavior patterns can be studied using time series analysis.

Time Series meaningful sentence

(41) The statistical package offers time series analysis capabilities.

(42) Parameter estimation is an important step in time series analysis.

(43) The statistical package allows me to conduct time series analysis.

(44) The Gaussian smoothing filter was applied to the time series data.

(45) The subsampling approach is commonly used in time series analysis.

(46) Stochastic processes are used in the analysis of time series data.

(47) AIC is a valuable tool for model selection in time series analysis.

(48) The normalizer is a valuable tool for normalizing time series data.

(49) Autocorrelation can be used to detect periodicity in a time series.

(50) Autocorrelation can be used to detect seasonality in a time series.

Time Series sentence examples

(51) The interpolator is a powerful tool for analyzing time series data.

(52) I prefer using the xts package over other time series packages in R.

(53) The power spectrum is often used in the analysis of time series data.

(54) Discretizing the time series data helps identify patterns and trends.

(55) Differencing is a common pre-processing step in time series analysis.

(56) The interpolative technique is commonly used in time series analysis.

(57) The iid assumption is often used in the analysis of time series data.

(58) The feature vector can be used to detect patterns in time series data.

(59) The differenced array is an essential tool in time series forecasting.

(60) The Markov property is a key concept in the study of time series data.

Sentence with time series

(61) Interpolative algorithms are used to fill in gaps in time series data.

(62) Preprocess the time series data by resampling it to a lower frequency.

(63) The concept of ergodicity is used in the analysis of time series data.

(64) Discretizing the time series data helped identify patterns and trends.

(65) The ergodicity of a time series allows us to make reliable predictions.

(66) Wavelets can be used to analyze and process financial time series data.

(67) Differencing is an essential step in time series modeling and analysis.

(68) The intercept upon a time series can be used to forecast future values.

(69) The segment structure of the array can be used for time series analysis.

(70) The differenced array is a powerful tool for analyzing time series data.

Use time series in a sentence

(71) Differencing can be used to identify and remove trends in a time series.

(72) I have found Gretl to be a powerful tool for analyzing time series data.

(73) The concept of isometry is also used in the analysis of time series data.

(74) The interpolator can be used to estimate missing values in a time series.

(75) Circulant matrices have applications in the analysis of time series data.

(76) The data points in the time series were generated by a stationary process.

(77) Interpolation is used in time series analysis to fill in gaps in the data.

(78) The LCS algorithm can be used to compare time series data for forecasting.

(79) The nonstationary time series data required advanced statistical analysis.

(80) The time series data exhibited the characteristics of a stationary process.

Sentence using time series

(81) The concept of a stationary process is fundamental in time series analysis.

(82) By using a sliding window, we can detect patterns in a time series dataset.

(83) Using a sliding window, we can identify anomalies in a time series dataset.

(84) Differencing is a powerful tool for analyzing and modeling time series data.

(85) Ergodic processes are used in signal processing to analyze time series data.

(86) The feed forward into approach is suitable for time series prediction tasks.

(87) Tensors are used in time series analysis to model and predict future trends.

(88) The statistical package provides advanced features for time series analysis.

(89) The assumption of exchangeability is often violated in time series analysis.

(90) The time series data was discretized into daily averages for trend analysis.

Time Series example sentence

(91) The mean square fluctuation is a measure of the variability of a time series.

(92) The xts object allows for easy manipulation and analysis of time series data.

(93) The xts package is a must-have for anyone working with time series data in R.

(94) The sigmoid function is used in time series analysis to model growth patterns.

(95) Normalizers are used in time series analysis to remove trends and seasonality.

(96) Differencing is a key concept in understanding and analyzing time series data.

(97) Differencing is a powerful tool in analyzing and forecasting time series data.

(98) The Markov assumption is often used in econometrics to model time series data.

(99) Pattern-matching is a powerful tool for analyzing patterns in time series data.

(100) The team will exponentiate over the time series data to forecast future values.

Sentence with word time series

(101) The scatter with a time series analysis revealed seasonal patterns in the data.

(102) Interpolations can be used to estimate missing values in a time series dataset.

(103) The feed forward model outperforms other algorithms in time series forecasting.

(104) Gunsmoke is the longestrunning live-action scripted American prime-time series.

(105) The z-axis is used in time series analysis to represent the temporal dimension.

(106) Convolutional architectures have been applied to time series forecasting tasks.

(107) Parameter estimation is used in time series analysis to forecast future values.

(108) The leptokurtic distribution is commonly observed in financial time series data.

(109) I am currently exploring different statistical methods for time series analysis.

(110) The Fourier series can be used to analyze the periodicity of a time series data.

Sentence of time series

(111) Seasonal adjustment is a statistical technique used to analyze time series data.

(112) The time series data was discretized into daily values for forecasting purposes.

(113) Discretizing the time series data allowed for easier forecasting and prediction.

(114) Linear regression can be used to identify trends or patterns in time series data.

(115) Autocorrelation is a fundamental concept in time series analysis and forecasting.

(116) Differencing can be applied to both univariate and multivariate time series data.

(117) The multi-layer perceptron regressor is commonly used in time series forecasting.

(118) In time series analysis, parameter estimation helps in forecasting future values.

(119) Differencing can be used to transform a non-linear time series into a linear one.

(120) Statistical estimation is used in time series analysis to forecast future values.

Time Series used in a sentence

(121) To analyze the time series data, we need to autocorrelate with the lagged values.

(122) Time series forecasting is used to predict future values based on historical data.

(123) Using a sliding window, we can perform time series forecasting with high accuracy.

(124) The time series data can be quantized into hourly or daily intervals for analysis.

(125) The extrema array can be used to identify critical points in a time series dataset.

(126) Differencing is a key step in the Box-Jenkins methodology for time series analysis.

(127) The assumption of a stationary process simplifies the analysis of time series data.

(128) The process of differencing can help identify and remove outliers in a time series.

(129) Differencing is often used in signal processing to remove noise from a time series.

(130) The process of differencing can help improve the accuracy of time series forecasts.

Time Series sentence in English

(131) The financial analyst used time series regressions to forecast future stock prices.

(132) Weather forecasting relies on time series models to predict future weather patterns.

(133) The principle of exchangeability is widely used in the analysis of time series data.

(134) The function interpolates the missing values by considering the time series pattern.

(135) The calculus of finite differences is often used in the analysis of time series data.

(136) Convolutional networks have been applied to time series data in financial forecasting.

(137) Differencing is particularly useful when dealing with non-stationary time series data.

(138) The algorithm interpolates the missing values by applying a time series decomposition.

(139) The process of differencing can help identify and remove seasonality in a time series.

(140) The spectral density plot can reveal hidden patterns or periodicities in a time series.

(141) The resampling process can be applied to time series data to detect patterns or trends.

(142) Differencing is an important step in building ARIMA models for time series forecasting.

(143) The time series analysis confirmed that the data can be modeled as a stationary process.

(144) The algorithm provides a way to marginalize out the hidden states in a time series model.

(145) LAMMA can be applied to regression, classification, and time series forecasting problems.

(146) Differencing can be used to transform a non-stationary time series into a stationary one.

(147) Multivariate analysis is used in time series analysis to model and forecast future values.

(148) Regularization can be applied to time series models to improve their forecasting accuracy.

(149) The process of differencing can help identify and remove autocorrelation in a time series.

(150) The confidence limits of the time series analysis indicated a trend in the data over time.

(151) The backfitting algorithm is often used in time series analysis to forecast future values.

(152) Time series analysis is essential in monitoring and predicting energy consumption patterns.

(153) The chronocorrelation is a statistical measure of the relationship between two time series.

(154) Interpolations can be used to estimate values at unmeasured times in a time series dataset.

(155) The process of differencing involves subtracting consecutive observations in a time series.

(156) Differencing can also be used to identify the presence of autocorrelation in a time series.

(157) Accuracy evaluation is essential in time series forecasting to ensure accurate predictions.

(158) The hierarchical clustering algorithm can be used to identify patterns in time series data.

(159) The secondorder autoregressive model was used to forecast future values of the time series.

(160) As an econometrician, he specialized in time series analysis to predict stock market trends.

(161) A stationary process is often used as a benchmark in comparing different time series models.

(162) Differencing can help identify and remove any remaining trends or patterns in a time series.

(163) The circulant structure of time series data allows for efficient forecasting and prediction.

(164) The subsampling approach is often employed in time series analysis to handle large datasets.

(165) The spectral decomposition of a time series can help identify periodic patterns in the data.

(166) Time series analysis is used in quality control to monitor and predict manufacturing defects.

(167) The central value of the time series data indicates the average value over a specific period.

(168) The study of time series analysis involves analyzing stochastic processes in continuous time.

(169) The process of differencing can help identify and remove heteroscedasticity in a time series.

(170) The use of autoregressive models can help to account for autocorrelation in time series data.

(171) The time series analysis confirmed that the data can be characterized as a stationary process.

(172) Time series data is used in environmental monitoring to study long-term changes in ecosystems.

(173) Time series models are used in agriculture to predict crop yields and plan farming activities.

(174) The econometrist's expertise in time series analysis helped predict stock market fluctuations.

(175) Neuroevolutionary algorithms can be used to evolve neural networks for time series prediction.

(176) Time series analysis is crucial in monitoring and predicting the spread of infectious diseases.

(177) The mean square difference between two time series can be used to detect patterns or anomalies.

(178) Differencing is a valuable tool for understanding the dynamics and patterns in time series data.

(179) The confidence limits of the time series forecasting provided a range of possible future values.

(180) Time series data can be used to analyze customer behavior and make targeted marketing strategies.

(181) Time series forecasting is used in sales forecasting to predict future sales and plan production.

(182) The econometrist's expertise in time series forecasting helped businesses make informed decisions.

(183) The programmer developed a function for interpolating missing timestamps in a time series dataset.

(184) Autocorrelating with the first-order lag can help us determine the persistence of the time series.

(185) The analysis of time series data helps in understanding patterns and trends over a specific period.

(186) Time series analysis is widely used in economics to study economic indicators and make predictions.

(187) The autocorrelation plot can help identify the presence of a trend or seasonality in a time series.

(188) Differencing is a fundamental concept in time series analysis and is widely used in various fields.

(189) As an econometrician, she used time series analysis to forecast future energy consumption patterns.

(190) The data scientist employed spectral zoom to analyze the spectral density of a time series dataset.

(191) Time series data is used in financial markets to analyze price movements and make trading decisions.

(192) Time series analysis is used in crime prediction to identify high-risk areas and allocate resources.

(193) Differencing is a statistical technique used to remove trends and seasonality from time series data.

(194) The concept of a random walk is widely used in the field of econometrics to analyze time series data.

(195) The function space of a time series analysis model defines the set of possible forecasting functions.

(196) The autocorrelation function is commonly used in time series analysis to identify patterns and trends.

(197) Time series data is used in sports analytics to analyze player performance and make strategic decisions.

(198) Time series models are used in energy markets to predict electricity prices and optimize energy trading.

(199) Time series forecasting is used in tourism planning to predict visitor arrivals and plan infrastructure.

(200) The identification of a stationary process is crucial for determining the appropriate time series model.

(201) The identification of a stationary process is essential for understanding the dynamics of a time series.

(202) Time series analysis is used in transportation systems to predict travel times and optimize traffic flow.

(203) The concept of exchangeability is closely related to the concept of stationarity in time series analysis.

(204) The results of differencing can be interpreted as the changes in the values of the time series over time.

(205) Time series models are used in traffic management to predict traffic congestion and plan efficient routes.

(206) Time series analysis is used in healthcare to monitor patient vital signs and predict disease progression.

(207) Time series forecasting is used in workforce management to predict staffing needs and optimize scheduling.

(208) With the help of a pattern matching algorithm, the software can detect anomalies in a time series dataset.

(209) To analyze the relationship between two time series data, we need to cross-correlate for their lag values.

(210) Time series forecasting is used in supply chain management to predict demand and optimize inventory levels.

(211) Time series models are used in telecommunications to predict network traffic and optimize network capacity.

(212) Time series data is used in asset management to analyze investment performance and make portfolio decisions.

(213) We can use the subsequences array to determine the presence of a specific subsequence in a time series data.

(214) Although autocorrelation can be a useful tool in time series analysis, it can also lead to biased estimates.

(215) Time series forecasting is used in demand planning to predict future customer demand and optimize production.

(216) Time series analysis is used in epidemiology to study disease outbreaks and make public health interventions.

(217) The econometrician's expertise in time series analysis helped identify trends and patterns in financial data.

(218) Time series data is used in social media analytics to analyze user behavior and make targeted recommendations.

(219) Differencing can help make a time series stationary, which is often a requirement for many statistical models.

(220) The regression coefficient can be used to assess the linearity assumption of the time series regression model.

(221) The stock market is often analyzed using time series techniques to identify potential investment opportunities.

(222) Time series models are used in transportation planning to predict future travel demand and plan infrastructure.

(223) Time series analysis is used in climate science to study long-term climate patterns and make climate predictions.

(224) The choice of the lag for differencing depends on the specific characteristics of the time series being analyzed.

(225) The regression coefficient can be used to assess the stationarity assumption of the time series regression model.

(226) The goal of differencing is to transform a time series into a stationary process that can be analyzed more easily.

(227) The first-order differencing of a time series involves subtracting each observation from its previous observation.

(228) The regression coefficient can be used to assess the autocorrelation assumption of the time series regression model.

(229) The autocorrelation analysis can be used to test for the presence of any serial correlation in the time series data.

(230) The regression coefficient can be used to assess the multicollinearity assumption of the time series regression model.

(231) The stop condition for this time series forecasting model is when the predicted values match the actual values closely.

(232) The regression coefficient can be used to assess the heteroscedasticity assumption of the time series regression model.

(233) Bessel's inequality is a result in time series analysis that relates the autocorrelation function to the power spectrum.

(234) The choice of the lag for differencing should be based on the specific characteristics of the time series being analyzed.

(235) The second-order differencing of a time series involves taking the first-order differences of the first-order differences.

(236) The third-order differencing of a time series involves taking the second-order differences of the first-order differences.

(237) The fourth-order differencing of a time series involves taking the third-order differences of the first-order differences.

(238) The fifth-order differencing of a time series involves taking the fourth-order differences of the first-order differences.

(239) The autocorrelation plot can be used to visualize the degree of correlation between the time series and its lagged values.

(240) Time series analysis is used in educational research to study student performance and identify effective teaching strategies.

(241) The autocorrelation function can be used to determine the degree of correlation between the time series and its lagged values.

(242) The autocorrelation coefficient can be used to quantify the degree of correlation between the time series and its lagged values.

(243) Autocorrelation can be positive or negative, depending on whether the values of a time series tend to be similar or dissimilar over time.

(244) Autocorrelation can be a useful tool for analyzing time series data, but it can also lead to spurious results if not properly accounted for in the analysis.

(245) While autocorrelation can be a useful tool for identifying patterns in time series data, it can also lead to overfitting and other issues if not properly accounted for.

The word usage examples above have been gathered from various sources to reflect current and historical usage of the word Time Series. They do not represent the opinions of TranslateEN.com.