# Best 5 segment piecewise linear fit

I'm trying to get a piecewise linear best-fit for the closing price of one of the stocks I'm interested in. The logic seems ok, and the workflow works for a straight-line (ie 2 pts, ie 1 segment) regression (actually NMinimize)... but if I increase the the number of variables to solve for, it breaks and complains about "abscissa"... the code is below with the comments. Also here is the data file . Can you help me figure out whats wrong? (also here is the nb file in case you need it).


In[120]:= (d =
Import["data_out.txt", "CSV"]);

In[121]:= d =
d[[4533 ;; 4922,
5]]; (*get close price data for 14-may from the dataset*)

In[122]:= d =
Flatten[{Table[i, {i, 390}],
d}, {2}]; (*add an index for the price ie 1-390 data pts*)

In[123]:= (* create objective function to minimize:
1. piecewise linear interpoltion function takes a set of points "p"
2. applies function to the index
3. subtracts the close price
4. squares the diffrenence
5. sum
6. root *)

In[124]:=
e[p_] := Total[(Interpolation[p, InterpolationOrder -> 1]@
d[[All, 1]] - d[[All, 2]])^2]^0.5

In[125]:= (*this is the solution i got using excel solver*)

In[126]:=
excelsolution = {{1, 32.69967765}, {28.16280834,
31.37817608}, {108.0001043, 32.75429029}, {135.5658831,
31.7584233}, {299.8762066, 32.76192525}, {390, 32.88427106}};

In[127]:= e[excelsolution]

Out[127]= 2.13146

In[128]:= ListPlot[{d, excelsolution}, Joined -> {False, True},
PlotMarkers -> {{Automatic, Tiny}, {Automatic, Small}}] (* run to see it *)

Out[128]= (*graphics pasted above*)

In[129]:= (*this works*)

In[130]:= NMinimize[
{
e[{{1, y0}, {390, y390}}],
31.5 <= y0 <= 33 && 31.5 <= y390 <= 33
},
{y0, y390},
Method -> "DifferentialEvolution"
]

Out[130]= {5.51241, {y0 -> 31.7968, y390 -> 32.8737}}

In[131]:= (*but this doesn't work when i increase the number of \
arguments*)

In[132]:= NMinimize[
{
e[{{1, y0}, {x1, y1}, {390, y390}}],
31.5 <= y0 <= 33 && 1 <= x1 <= 390 && 31.5 <= y1 <= 33 &&
31.5 <= y390 <= 33
},
{y0, x1, y1, y390},
Method -> "DifferentialEvolution"
]

During evaluation of In[132]:= Interpolation::indat: Data point {x1,y1} contains abscissa x1, which is not a real number.

During evaluation of In[132]:= Interpolation::indat: Data point {x1,y1} contains abscissa x1, which is not a real number.

During evaluation of In[132]:= Interpolation::indat: Data point {x1,y1} contains abscissa x1, which is not a real number.

During evaluation of In[132]:= General::stop: Further output of Interpolation::indat will be suppressed during this calculation.

During evaluation of In[132]:= NMinimize::nnum: The function value {10.8271,10.8107,10.7944,10.7781,10.7618,10.7456,10.7294,10.7133,<<35>>,10.157,10.1423,10.1276,10.113,10.0984,10.0839,10.0694,<<340>>} is not a number at {x1,y0,y1,y390} = {285.476,32.6896,32.3246,32.9648}.


• Are the join points known? Or as they chosen after looking at the data? I ask because if they are unknown, then they must be estimated from the data.
– JimB
Commented May 18, 2020 at 22:51

Here is an approach based on generating a Line object, determining the total distance from all points in your dataset to the line, and minimizing that distance. In the following, price is obtained from your data, similar to what you described in your code:

price = Import["data_out.txt", "CSV"][[4533 ;; 4922, 5]];
price = Transpose@{Range[Length[price]], price}


First, let's define a target function to minimize:

ClearAll[model]

model[data_, positionlist_] /;
(Max[positionlist] >= Length[data] || Min[positionlist] < 1) := 10.^10

model[data_, positionlist_?(VectorQ[#, NumericQ] &)] := Module[{rdf},
rdf = RegionDistance[Line@data[[Join[{1}, Sort@positionlist, {-1}] ]] ];
Total@rdf[data]
]


The minimization is carried out using NArgMin using the Simulated Annealing algorithm with a very high perturbation scale setting to explore a wider swath of the parameter space. Such a high value of the perturbation scale generates quite a few "unacceptable" values (e.g. negative, or larger than the size of the dataset); rather than introducing a constraint, which slows down the minimization significantly, I introduced "guard definitions" in the target model function, as shown above, that return an extremely high value.

min = Sort@Round@
NArgMin[
model[price, Round@{a, b, c, d}],
{a, b, c, d},
Method -> {
"SimulatedAnnealing",
"PerturbationScale" -> 100
}
]


Here is the resulting line overlaid on the points:

ListPlot[
price,
Epilog -> {
PointSize[0.02],
Through[{Point, Line}@price[[{1, Sequence @@ min, -1}]]]
}
]


Although this approach does not require a manual choice of starting conditions as required, I want to point that many similar minima are present, so even very small changes in any of the parameters may provide rather significantly different results. Unfortunately, this seems to be a feature of the problem, rather than of the minimization process.

• Hi MarcoB... thanks for the alternative workflow....but the idea here is to fully automate the "simplification" of the stock price movements using a 5-segmet piecewise linear function.... the key-word is "fully automate"... I don't want to give it initial values manually... Commented May 19, 2020 at 21:15
• @CuriousDudeFromEgypt See the edit. Now without manually selected starting values! Commented May 19, 2020 at 22:43

The reason you are receiving the "Data point {x1,y1} contains abscissa x1, which is not a real number" error is that Interpolation expects a numerical value for the abscissa.

In the first example for the end points you used 1 and 390. This worked fine.

In the second example you used, in addition, x1.

That was what Interpolation was complaining about.

It may be overdoing it but functions were defined so that all arguments were required to be numeric.

## Initialization

dataOut = Import["data_out.txt", "CSV"];
d = dataOut[[4533 ;; 4922, 5]];


A function was created using the Excel results.

intFunExcel = Interpolation[excelsolution, InterpolationOrder -> 1]


and then the data and Excel function were plotted.

Show[
ListPlot[
d,
PlotStyle -> Red,
PlotMarkers -> {Automatic, Tiny}
],
Plot[
intFunExcel[x],
{x, 1, 390},
PlotStyle -> Black
]
]


## NMinimize - numeric functions

This was broken into two parts, first the function that will return the y value and then secondly the error function.

Given an x value, curveFitIntF will return a y value. It needs the x input value and six data points (the first and last x values are 1 and 390).

curveFitIntF[
x_?NumericQ,
y1_?NumericQ,
{x2_?NumericQ, y2_?NumericQ},
{x3_?NumericQ, y3_?NumericQ},
{x4_?NumericQ, y4_?NumericQ},
{x5_?NumericQ, y5_?NumericQ},
y390_?NumericQ
] := Interpolation[
{
{ 1.0, y1},
{x2, y2},
{x3, y3},
{x4, y4},
{x5, y5},
{390.0, y390}
},
InterpolationOrder -> 1][x]


Next define the error function. Note: The error itself rather than square root was used.

error[ y1_?NumericQ,
{x2_?NumericQ, y2_?NumericQ},
{x3_?NumericQ, y3_?NumericQ},
{x4_?NumericQ, y4_?NumericQ},
{x5_?NumericQ, y5_?NumericQ},
y390_?NumericQ] := Total[
Map[
(curveFitIntF[#[[1]], y1, {x2, y2}, {x3, y3}, {x4, y4}, {x5, y5},
y390] - #[[2]])^2 &,
d]
]


Next, run it through NMinimize.

Note: NMinimize really needs some reasonable starting points for the parameters. This is quite important.

I eyeballed them from the graph and used +/- 10 for the x value and (much smaller) +/- 0.1 for the y value.

seg5 = NMinimize[
{
error[
y1,
{x2, y2},
{x3, y3},
{x4, y4},
{x5, y5},
y390
],
1.0 < x2 < x3 < x4 < x5 < 390.0 &&
31 < y1 < 33 &&
31 < y2 < 33 &&
31 < y3 < 33 &&
31 < y4 < 33 &&
31 < y5 < 33 &&
31 < y390 < 33
},
{
{y1, 32.6, 32.8},
{x2, 25.0, 35.0},
{y2, 31.3, 31.5},
{x3, 90.0, 110.0},
{y3, 32.7, 32.9},
{x4, 110, 130},
{y4, 31.6, 31.8},
{x5, 290, 310},
{y5, 32.6, 32.8},
{y390, 32.7, 32.9}
},
Method -> "DifferentialEvolution"
]


The results were:

{4.51966, {y1 -> 32.6719, x2 -> 27.7708, y2 -> 31.3766, x3 -> 107.248,
y3 -> 32.7467, x4 -> 136.495, y4 -> 31.7585, x5 -> 299.32,
y5 -> 32.7626, y390 -> 32.8829}}


This produced an error very slightly smaller than the excel results.

A function is defined to use the seg5 results.

intFun = Interpolation[
{
{ 1.0, y1},
{x2, y2},
{x3, y3},
{x4, y4},
{x5, y5},
{390.0, y390}
},
InterpolationOrder -> 1] /. seg5[[2]]


One can ignore the warning message.

Now plot it:

Show[
ListPlot[
d,
PlotStyle -> Red,
PlotMarkers -> {Automatic, Tiny}
],
Plot[
intFun[x],
{x, 1, 390},
PlotStyle -> Black
]
]
`

• Hi Jack... I wanted NMinimize to 'look for' the right X,Y pairs instead of me telling it where to start... I thought the way NMinimize works by initially "guessing" values for the variables its trying to solve-for and iterating/assessing the fitness of those guesses and improving with each iteration.... ie, the objective function (which is effectively an interpolation rms) should receive "real values" for each variable on each iteration....and hence, I don't know why its complaining? does this make sense? Commented May 19, 2020 at 21:13
• In the bottom of the Help documentation for NMinimize there is a section for Tutorials. It demonstrates why, depending upon the problem, good starting values may be required. I didn't try this problem with no starting values. Why don't you give it a try and see if it works. I tried to explain why in the text you are getting the abscissa warning. Try reading it again. Further there are many posts on this site explaining that the optimizers may start with a symbolic solution before using numerical values. Defining function that only accept numeric values is the workaround. Commented May 19, 2020 at 23:42
• I used functions that only accept numeric arguments to circumnavigate the Interpolation problem. Commented May 19, 2020 at 23:43
• Yes...it worked when I defined the function to take inputs ala e[x1_Real, y1_Real]:=...etc so that's cool :) .... a couple more questions pls 1. is there a way to define a function that only accepts lists of real numbers? 2. Is there a way to feed NMinimize some starting/initial values? 3. I noticed that 1<x1<x2<x3<x4<390 constraints are not being honored, actually being ignored... any idea why? Commented May 20, 2020 at 2:44