Convert Financial Time Series Objects (fints) to Timetables

Convert Financial Time Series Objects (`fints`) to Timetables

In R2023a, financial time series (fints), and its associated methods have been removed and are replaced with a MATLAB^® timetable function. If you use fints or the associated methods, you receive an error. To help you convert from the older fints to the newer timetable functionality, use the following information.

Create Time Series

I/O Related Operations

Task	Removed Functionality	Use This Functionality
Construct by passing in data and dates	`fints(dates,data,datanames)`	Use `timetable`: `timetable(rowTimes,var1,...,varN,'VariableNames',{'a','b',...})`
Construct by conversion of files	`ascii2fts(filename,descrow,colheadrow,skiprows)`	Use `readtable` and `table2timetable`: `T = readtable(filename,opts,Name,Value)` `TT = table2timetable(T,'RowTimes',timeVarName)`
Write files	`fts2ascii(filename,tsobj,exttext)`	Use `writetable`: `writetable(TT,filename)`
Convert to matrix	`fts2mat(tsobj)`	`S = vartype('numeric');` `TT2 = TT(:,S)` `TT2.Variables`

Index an Object

Indexing an Object

Task	Removed Functionality	Use This Functionality
Indexing with a date	`myfts('05/11/99')`	`TT({'1999-05-11'},:)`
Indexing with a date range	`myfts ('05/11/99::05/15/99')`	Use `timerange`: `S = timerange('1999-05-11','1999-05-15');` `TT2 = TT(S,:)`
Indexing with integers for rows	`myfts.series2(1)` `myfts.series2([1, 3, 5])` `myfts.series2(16:20)`	`TT(1,{'series2'})` `TT([1, 3, 5],{'series2'})` `TT(16:20,{'series2'})`
Contents of a specific time field	`myfts.times`	Use `timeofday`: `timeofday(TT.Properties.RowTimes)`
Contents for a specific field in a matrix	`fts2mat(myfts.series2)`	`TT.series2`

Transform Time Series

Assume that all variables are numeric within a timetable, or the operations can be applied on TT2:

S = vartype('numeric');

TT2 = TT(:,S)

Filter Time Series

Task	Removed Functionality	Use This Functionality
Boxcox transformation	`newfts = boxcox(oldfts)`	Use `boxcox`: `TT.Variables = boxcox(TT.Variables)`
Differencing	`diff(myfts)`	`TT{2:end,:} = diff(TT.Variables)` `TT(1,:) = []`
Indexing with integers for rows	`fillts(oldfts,fill_method)`	Use `fillmissing`: `fillmissing(TT,method)` (Assumes no missing dates)
Linear filtering	`filter(B,A, myfts)`	Use `filter`: `TT.Variables = filter(b,a,TT.Variables)`
Lag or lead time series object	`lagts(myfts,lagperiod)` `leadts(myfts,leadperiod)`	Use `lag`: `lag(TT,lagperiod)` `lag(TT,-leadperiod)` (Assumes a regularly spaced timetable)
Periodic average	`peravg(myfts)`	Use `retime`: `retime(TT,newTimes,'mean')`
Downsample data	`resamplets(oldfts,samplestep)`	Use `retime`: `retime(TT,newTimeStep,method)`
Smooth data	`smoothts(input)`	Use `smoothdata`: `smoothdata(TT)`
Moving average	`tsmovavg(tsobj,method,lag)`	Use `movavg`: `movavg(TT,type,windowSize)`

Convert Time Series

Assume that all variables are numeric within a timetable, or the operations can be applied on TT2:

S = vartype('numeric');

TT2 = TT(:,S)

Conversion Operations

Task	Removed Functionality	Use This Functionality
Convert to specified frequency	`convertto(oldfts,newfreq)`	Use `retime`: `retime(TT,newTimeStep,method)`
Convert to annual	`toannual(oldfts,...)`	Use `convert2annual`: `convert2annual(TT,…)`
Convert to daily	`todaily(oldfts,...)`	Use `convert2daily`: `convert2daily(TT,…)`
Convert to monthly	`tomonthly(oldfts,...)`	Use `convert2monthly`: `convert2monthly(TT,…)`
Convert to quarterly	`toquarterly(oldfts,...)`	Use `convert2quarterly`: `convert2quarterly(TT,…)`
Convert to semiannual	`tosemi(oldfts,...)`	Use `convert2semiannual`: `convert2semiannual(TT,…)`
Convert to weekly	`toweekly(oldfts,...)`	Use `convert2weekly`: `convert2weekly(TT,…)`

Merge Time Series

Merge Operations

Task Removed Functionality Use This Functionality

Merge multiple time series objects

Task	Removed Functionality	Use This Functionality
Merge multiple time series objects	`merge(fts1,fts2)`	`[TT1;TT2]` (requires variable name to be the same) `unique(TT)`
Concatenate financial time series objects horizontally	`horzcat(fts1,fts2)` or `[fts1,fts2]`	Use `horzcat` or `synchronize`: `horzcat[TT1,TT2]` (requires variable name to be the same) or `synchronize(TT1,TT2)`
Concatenate financial time series objects vertically	`vertcat(fts1,fts2)` or `[fts1;fts2]`	Use `vertcat`: `vertcat[TT1;TT2]`

merge(fts1,fts2)

[TT1;TT2] (requires variable name to be the same)

unique(TT)

Concatenate financial time series objects horizontally

horzcat(fts1,fts2) or [fts1,fts2]

Use horzcat or synchronize:

horzcat[TT1,TT2] (requires variable name to be the same) or

synchronize(TT1,TT2)

Concatenate financial time series objects vertically

vertcat(fts1,fts2) or [fts1;fts2]

Use vertcat:

vertcat[TT1;TT2]

Analyze Time Series

Due to flexibility of a timetable that can hold heterogeneous variables, a timetable does not support math operations or descriptive statistical calculations. If you would like to apply any numeric calculations on a timetable, use the following guidelines.

Assume that all variables are numeric within a timetable, or the operations can be applied on TT2:

S = vartype('numeric');

TT2 = TT(:,S)

Descriptive Statistics and Arithmetic and Math Operations

Task Removed Functionality Use This Functionality

Extract out numerical data

Task	Removed Functionality	Use This Functionality
Extract out numerical data	`srs2 = myfts.series2`	`TT.Variables`
Apply some options (statistics)	For example: `min`, `max`, `mean`, `median`, `cov`, `std`, and `var`	Use `cov` (or `max`, `mean`, `median`, `min`, `std`, or `var`): `cov(TT.Variables)`
Apply some options (operations)	For example: `sum` and `cumsum`	Use `sum` or `cumsum`: `TT.Variables = cumsum(TT.Variables)`

srs2 = myfts.series2

TT.Variables

Apply some options (statistics)

For example: min, max, mean, median, cov, std, and var

Use cov (or max, mean, median, min, std, or var):

cov(TT.Variables)

Apply some options (operations)

For example: sum and cumsum

Use sum or cumsum:

TT.Variables = cumsum(TT.Variables)