Find Change Points Find abrupt changes in data Smooth Data Smooth noisy data Find Local Extrema Find local maxima and minima Clean Missing Data Find, fill, or remove missing data Normalize Data Center and scale data Clean Outlier Data Find, fill, or remove outliers Remove Trends Remove polynomial trend from data Join Tables Combine two tables using key variables Stack Table Variables Combine values from multiple table variables into one table variable Unstack Table Variables Distribute values from one table variable to multiple table variables Retime Timetable Resample or aggregate timetable data Synchronize Timetables Retime and combine timetables to new time vector Compute by Group Summarize, transform, or filter by group All numeric variables All supported variables Asymmetric Centered Cleaned data Cleaning method Constant value Select data Default value Days Hours Input data Workspace variable Linear interpolation Modified Akima cubic interpolation Specified variables Mean Median Specify method Specify method and parameters Milliseconds Minutes Months Moving mean Moving median Moving window Nearest value Next value None Visualization is not supported for input {0} Visualization is not supported for any of the selected variables in {0} Removed by other variables Output table Specify parameters Shape-preserving cubic interpolation (PCHIP) {0} variable with supported plot functionality Previous value Output format Create new {0} from selected variables Logical indices for only selected variables Copy {0} with cleaned selected variables Extract {0} from {1} Logical indices for {0} Quarters X-axis A {0} variable that is finite and in ascending order X-axis associated with the input data. Default is [1 2 3 ...]. Values must be finite and in ascending order. The X-axis must be the row times of the timetable when output is a timetable The X-axis must be the row times of the timetable when output is logical indices for multiple variables Seconds Select variable selected variable Spline interpolation {0} variable from {1} Variable to display Display results Weeks Window units before current element Window units after current element Window units centered about current element Window type Window unit type Years Change points Find change points Linear Linear regime Maximum number of change points Increase to allow for more possible change points Number of change points Segment mean Plot change points Plot segments between change points Segments between change points Abrupt changes in linear regime in {0} Abrupt changes in mean in {0} Abrupt changes in variance in {0} Threshold Control amount of change points Increase threshold to reduce number of change points Type of change Variance Degree of the polynomial in the Savitzky-Golay filter Gaussian filter Local linear regression (Lowess) Local quadratic regression (Loess) Table variables must be non-empty, numeric or logical column vectors. Integer variables must be real. Polynomial degree Robust Lowess Robust Loess Smoothing factor Increase for more smoothing Smoothing method Savitzky-Golay polynomial filter Smoothed data Smooth input data Smoothed noisy data in {0} using the {1} method a custom function All non-grouping variables All variables Provide bin edges Numeric workspace vector of length at least two, sorted in ascending order Datetime workspace vector of length at least two, sorted in ascending order Duration workspace vector of length at least two, sorted in ascending order Group data by {0} with bin edges {1} Group data into bins of {1} {2} using {0} Group data using unique values in {0} Group data into {1} bins using {0} Group data by {1} in {0} Select width of bins Century {0} is the data in a group from the computation data {0} is the data in a group from one computation variable {0} is true, false, or a logical column vector with the same height as {1} Example 1: Keep only groups with an average greater than 10 Example 2: Keep only nonoutliers in each group {0} is size 1 in the first dimension Example: Count the number of outliers in each group {0} is either the same size as {1} or is size 1 in the first dimension Example 1: Divide each group member by the group average Example 2: Replace group members with the group average Counts Custom function Select groups and data to compute on Compute on Variables of the input table to compute on Workspace variable to compute on Day Day name Day of month Day of week Day of year Decade Select or create a new local function Fill missing Function must return false to filter out group members and return true to keep group members Select computation for groups Filter data by group Filter\nby group Filter data in {0} by group Filter data in {0} grouped by {1} Filter data in {0} grouped by {1} and {2} Operations that remove groups or group members Group by Compute group summary Compute\nstats\nby group Compute {1} for each group in {0} Compute {1} for {0} grouped by {2} Compute {1} for {0} grouped by {2} and {3} Operations that provide a one-element output per group Transform data by group Transform\nby group {1} groups of data in {0} {1} data in {0} grouped by {2} {1} data in {0} grouped by {2} and {3} Operations that provide the same size output as input Grouping variable, or a table or timetable containing the grouping variable Function handle Hour Hour of day Included bin edge End of the bin interval that is inclusive Include empty groups Display groups containing no data Include missing groups Display groups where a grouping variable contains missing values such as NaN or NaT Input table Grouping variable or table or timetable containing the grouping variable Left Fill missing by interpolating group data Local function Maximum Mean Center to have mean 0 Center Fill missing with group mean Median Computation per group Computations per group {0} methods chosen Minimum Minute Minute of hour Mode Month Month name Month of year summary statistics New Group by unique values Scale by 2-norm Scale Number of non-zeros Select number of bins Number of missing Output table Range Quarter Quarter of year Replace values Output table with the transformed data in place of the original data Rescale range to [0 1] Rescale Right Second Second of minute Select all Input datatype does not allow this statistic Input is the data in a group. Output is size 1 in the first dimension. Standard deviation Sum Select unit to bin data Transform Input is the data in a group. Output matches the size of the input, or is size 1 in the first dimension. Variance Week Week of month Week of year Year Normalize using z-score Normalize All Maxima and minima Center Extrema type First Find local maxima Find local minima Flat selection Selected extrema for regions with identical consecutive extrema Last Local maxima Local minima Maxima Max. num. extrema Increase to allow for more possible extrema Minima Min. prominence Increase to require that detected extrema stand out more prominently relative to neighboring points Min. separation Increase to require more distance between detected extrema Number of extrema Define local extrema Plot local maxima Plot local minima Prominence window Local maxima and minima in {0} Local maxima in {0} Local minima in {0} Units for minimum separation and prominence window Units for constant fill value End values Units for constant end values Filled missing data Filled missing entries Fill missing data Fill missing Method to fill missing entries Find missing data Max gap to fill Gaps in data larger than this value will not be filled Units for gap size Missing entries Missing input data Specify non-standard indicators Table variables must be non-empty numeric, logical, string, categorical, cellstr, char, datetime, duration, or calendarDuration column vectors Number of filled missing entries Number of missing entries Number of removed missing entries Data in rows where other variables contain missing entries Plot filled missing entries Plot missing entries Plot data in rows where other variables contain missing entries Plot removed missing entries Remove missing data Removed missing entries Remove missing Same as fill method Standardize missing data Define optional missing value indicators Indicators Select indicators Standard indicators depend on the datatype. For example: NaN, NaT, ' ', <missing>, <undefined> List values to treat as missing, separated by commas.\nFor example: -99, "N/A" Inside single quotes, list values to treat as missing, separated by commas.\nFor example: ' -99, "N/A" ' Use only standard indicators Filled missing data in {0} using the {1} method Missing data in {0} Removed missing data from {0} Center Center and scale Center shift value Subtract the mean from the input data Subtract the median from the input data Subtract numeric value from input data Subtract workspace value from input data Center type First element From workspace infinity norm Interquartile range Median absolute deviation Mean Median Median IQR Normalization method Subtract a center shift value Subtract a center shift value then divide by a scale value Center and scale to have median 0 and interquartile range 1 Divide by the norm of the data Rescale to a new range Divide by a scale value Center and scale data using the z-score Norm Value p for p-norm or inf for infinity norm Normalized data Table variables must be non-empty, real double or single column vectors Numeric scalar Output center and scale values Return the center and scale used to do the normalization P-Norm {0}-norm Range Left limit Right limit Scale Divide by first element of data Divide by interquartile range Divide by median absolute deviation Divide by numeric value Divide by standard deviation Divide by workspace value Scale factor Scale type Standard deviation Center {0} to have {1} 0 Subtract {1} from {0} Center {0} to have {1} 0 and divide by {2} Subtract {1} from {0} and then divide by {2} Center and scale {0} to have {1} 0 and {2} 1 Scale range of {0} to {1} Divide {0} by {1} Separate axes Display the input data and normalized data on two separate subplots A numeric array in the workspace A numeric array or a table array with variable names that match the specified input variables Z-score Center and scale to have median 0 and median absolute deviation 1 Center and scale to have mean 0 and standard deviation 1 Z-score type Center value Additional outlier computations for plot Clip to threshold value Detection method Filled outliers Fill outliers Method to fill outliers Find outliers Generalized extreme studentized deviate (GESD) Grubbs Histogram Line Lower threshold Data points below this percentile are considered outliers Table variables must be non-empty, real double or single column vectors Number of outliers Number of outliers cleaned Data in rows where other variables contain outliers Outlier center Outliers Outlier thresholds Specify cleaning method Define outliers Percentiles Plot filled outliers Plot removed outliers Plot data in rows where other variables contain outliers Plot outlier center Plot outliers Plot outlier thresholds Plot style Quartiles Remove outliers Filled outliers in {0} using the {1} method Outliers in {0} Removed outliers from {0} Threshold factor Significance level of the outlier test. Increase to detect more outliers Data points outside this number of standard deviations are considered outliers Data points outside this number of scaled median absolute deviations (MAD) are considered outliers Data points outside this number of interquartile ranges are considered outliers Upper threshold Data points above this percentile are considered outliers Click to select and deselect breakpoints\nDrag to move existing breakpoints Breakpoints Specify breakpoints to remove a piecewise trend Number of breakpoints Select from workspace Clear breakpoints Constant Continuous Specify whether the trend must be continuous at the breakpoints Custom Cubic Polynomial type Degree of polynomial to be removed from data Click to remove breakpoint Detrended data Ignore missing data Linear Space equally NaN values are omitted when calculating the trend New breakpoint will be rounded to nearest sample point Number of missing entries in input data Specify trend parameters Quadratic Remove trend from data Select from plot Remove {0} trend from {1} Remove {0}th degree polynomial trend from {1} Trend Select data Specify join Left table Outer\njoin Horizontal\nconcatenation Inner\njoin Input tables \nJoin Merging variable Left\nouter join Combine merging variables Join tables Output table Right\nouter join Right table horizontal concatenation inner join join Combine {0} and {1} using {2} left outer join outer join right outer join vertical concatenation Append right table to left table when\nnumber of rows match and variables have distinct names Match rows from both tables with common key values\nand discard all other rows Key values must be common to both tables\nRetain all variables from the left table and append content from the right table Key variable used to match and combine data between the input tables Right key variable must be compatible with left key variable Match rows from both tables with common key values\nand include remaining rows from the left table Combine the left key variable and the right key variable into a single variable in the output table Match rows from both tables with common key values\nand include all other rows Match rows from both tables with common key values\nand include remaining rows from the right table Append bottom table to top table when\nvariable names match and datatypes are compatible Vertical\nconcatenation Count Custom First Maximum Mean Median Minimum Mode Aggregator for new table variable values Sum Unique Constant The values in this table variable will be replicated Values in new table variables The values in this table variable will be distributed into the new table column variables Discard This table variable is not included in the output Group by The unique values in this variable define groups of rows Names of new table variables The values in this table variable will be used as the names of the new table column variables This variable has too many unique elements to be used for names of new table variables All variables have too many unique elements to be used for names of new table variables Input table Keep first The values for this variable are taken from the first row in each group Select table Stack Select table variables to stack Stack data from multiple table variables in {0} into single variable Stack data from {0} and {1} in {2} into single variable Stack data from {0}, {1}, and {2} in {3} into single variable The values in this variable will be stacked Stack variables Preview limited to 50 rows Select table variable to unstack Unstack data from {0} in {1} into multiple variables Unstack variables Add Override general rule for one or more timetable variables Aggregate data Specify function that accepts a vector as input and returns a scalar or row vector Times from timetables Times within common range Concatenate and reorder timetable variables Make copies of timetable variables with duplicate names Retime each override variable to same time vector Save original order of timetable variables Constant Number of values Custom Time step Unique row times of input Fill values Fill with Constant Fill with missing Fill with nearest value Fill with next value Fill with previous value Times from first timetable First value in bin Function handle Input timetable Input timetables Interpolate data Intersection Times from last timetable Last value in bin Linear interpolation Maximum Mean Select method for adjusting data Method General rule Minimum Modified Akima cubic interpolation Months Specify new times Vector of datetime values Vector of duration values Output timetable Exceptions Input timetable Variable from input timetable Shape-preserving cubic interpolation (PCHIP) Product Quarters Retimed data Retime timetable Sample rate Sample rate units Times from workspace Selection method Spline interpolation Sum Retime {0} using {1} Synchronize {0} and {1} using {2} Synchronize several timetables using {0} aggregation a fill value interpolation the VariableContinuity property Synchronize timetables Time step units Fill new rows with a constant value Count the number of rows in each time bin Use function specified by a function handle Use the first value in each time bin Use linear interpolation Use the last value in each time bin Use modified Akima cubic Hermite interpolation Calculate the maximum of the values in each time bin Calculate the mean of the values in each time bin Calculate the minimum of the values in each time bin Fill new rows with a missing data indicator Copy the data from nearest row in the input timetable Copy data from the next row in the input timetable Use shape-preserving piecewise cubic interpolation Copy data from the previous row in the input timetable Calculate the product of the values in each time bin Use piecewise cubic spline interpolation Sum the values in each time bin Use per-variable methods specified by the VariableContinuity property of the input timetable. Union Use VariableContinuity property Weeks Degree Fill method Moving window type Selection method Table variable Table variables Smoothing parameter Units Window length Left half window length Right half window length