Changepoint detection (CPD) is the process of detecting changes in the probability distribution of a time series data. There are mainly two different directions of the usabiliy of CPD. First direction concerns with the subject of statistical quality control, where any kind of abrupt changes in the data generating process of a time series observation can be detected through CPD methods, which serves the purpose of alarming the concerning authority [1] . On the other hand, various time series modelling rely on CPD techniques to segment the study period, and then apply different modelling for each homogeneous segments [2] .
First CPD method was possibly proposed by Hinkley [3] based on the likelihood function and it was able to detect at most one changepoint present in the data. With a clustering approach, Scott and Knott [4] extended the idea to the detection of multiple changepoints based on Binary Segmentation, which was theoretically corroborated by a similar study of Sen and Srivastava [5] . Picard [6] extended the algorithm to a much general setup, using a generalized version of Kolmogorov-Smirnov statistic, as well as its simplification towards the mean and variance change in an autoregressive process. Some recent and popular algorithms for CPD include Pruned Exact Linear Time (PELT) [7] , Bayesian Change-point Detection analysis [8, 9, 10] , Kernel Change-point Detection [11] , Change-point detection using nonparametric statistical measures [12] . The mathematical setup of the problem of changepoint detection starts with a multivariate time series signal {y t } T t=1 , where each y t ∈ R p . There also exist some fixed points 1 < t * 1 < t * 2 < . . . t * k < T , such that within the interval t (i−1) , t i , the time series observations follow same probability distributional model, but these models differ from one interval to another. In particular,
and F i = F (i+1) for i = 0, 1, . . . (k−1). A special case follows when the signal is assumed to be piece-wise stationary. The problem of CPD deals with identifying the set of points {t 1 , t 2 , . . . t k } where the distribution or behavior of the signal changes, from the knowledge of the available time series observations y t 's. The knowledge of the number of changepoints k may or may not be known a-priori, in which case it also has to be estimated from the data.
A general framework for existing CPD algorithms is provided in a comprehensive review by Truong et al. [26] . Most of the existing algorithms involve minimization of a criterion function V (T , y) to obtain the best set of changepoints T ,
Fuzzy sets, proposed by Lotfi A. Zadeh [27] , provide a mathematical means of modelling vagueness and imprecise information. They are a generalization of crisp sets or normal sets. A fuzzy set is represented by an ordered pair X, µ . Denoting U as the universe of objects, and X ⊆ U, the membership function for the set X can be described as µ : U → [0, 1], indicating the degree on inclusion of an element x ∈ U into the set X. Consequently, 3 cases are possible based on the value of µ(x):
1. Not included in X, µ if µ(x) = 0.
2. Partially included in X, µ if 0 < µ(x) < 1.
3. Fully included in X, µ if µ(x) < 1.
While fuzzy sets have been extensively used in fields like control systems for electronic devices [28] , decision-making support systems in businesses [29] , prediction systems in finance [30] , quantitative pattern analysis for industrial quality assurance [31] , and uncertainty modelling in pattern recognition, image processing and vision [32] , its use in changepoint analysis and statistics, in general, has been very limited. In this paper, we shall model the membership of a data point to a particular distribution as a fuzzy set to characterize the ambiguity in the data generation process.
The notion of rough sets, as introduced by Pawlak [33] , is defined as follows: Let A = U, A be an information system, where U is the universe of all objects as mentioned before, and A be the set of attributes which are used to identify two elements of U. Let B ⊆ A be a subset of attributes and X ⊆ U. We can approximate the set X using only the information contained in B by constructing lower and upper approximations of X. Let So, the lower approximation of a set X relative to B in U are the elements in U which can be certainly classified as elements of X based on B. Intuitively, it is the set of all elements x in U such that the equivalence class containing x is a subset of target set X. In other words, the lower approximation is the set of objects that are certainly members of the target set X.
On the other hand, the upper approximation is the complete set of objects x such that the equivalence class containing x has non empty intersection with X. Intuitively, it is the set of elements in U/B that cannot be positively (i.e., unambiguously) classified as belonging to the complement of X of the target set X. In contrast to lower approximation, the upper approximation is the complete set of objects that are certainly as well as possibly members of the target set X.
The pair BX, BX denotes the rough representation of the crisp set X with respect to B. This rough representation actually captures the uncertainty in defining X in view of the incomplete information provided by the subset of attributes B. Pawlak [34] provided a numerical characterization of roughness of X as
So ρ B = 0 means, the set X is crisp or exact (with respect to B) and conversely, ρ B > 0 means, X is rough, i.e. ambiguous, with respect to B. This vague definition of X in U (in terms of lower and upper approximations) signifies incompleteness of knowledge about U.
Similar to the lower and upper approximation of a crisp set, definition of such approximations for a fuzzy set can also be established. While the definition of lower and upper approximations of a set may vary based on the nature of set X (crisp or fuzzy) as well as the nature of relation between elements of the universe U, here, we are only concerned with approximations of a fuzzy set X with respect to an indiscernibility relation R which is either an equivalence relation (crisp or fuzzy) or a tolerance relation (crisp or fuzzy). The expressions of upper and lower approximations for both these cases have been derived by Sen and Pal [25] . Since the set approximations obtained using equivalence relations are not always smooth, we will restrict our consideration only on the tolerance relations. The general implicit expressions of these approximations are given below, while a more explicit form of such approximations will be derived later in eqn. 4.
A tolerance relation (crisp or fuzzy) is a (crisp or fuzzy) relation which satisfies (crisp or fuzzy) reflexivity and symmetry. Unlike equivalence relations, tolerance relations are not necessarily transitive. When R is a tolerance relation, the space U, R is called tolerance approximation space. Associated with a tolerance relation R, there is a membership function S R of the relation itself, where S R (u, v) denotes the membership value of the pair (u, v) belonging to the relation R. With the help of these, the lower and upper approximations of any crisp or fuzzy set X are obtained as follows;
where the approximations of the membership functions are based on the tolerance function S R of the relation R;
where µ X , which takes values in the interval [0, 1], is the membership function associated with set X. When X is a crisp set, µ X would take values only from the set {0, 1}. Similarly, when R is a crisp tolerance function, S R (u, ψ) would take values only from the set {0, 1}.
Now, using the definitions of upper and lower approximations of a set, an entropy can be defined to quantify the ambiguity in description of X. While the roughness measure ρ B (X) as in eqn. 3 gives a measure of ambiguity in the description of X, they are needed to be transformed to properly describe information gain. In this regard, two types of gain functions as defined by Pal and Pal [35] are considered.
This is derived by using logarithmic function to measure the gain in incompleteness similar to "gain in information" in Shanon's entropy. The logarithmic entropy measure for quantifying the incompleteness of knowledge about U with respect to the definability of a set X ∈ U is given as
where for any set D ∈ U,
. Note that the "gain in incompleteness" term is taken as
and for β > 1 it takes values in [1, ∞].
The other kind of entropy measure is defined using exponential function for the "gain in incompleteness". This class of entropy is derived using
. So the expression of entropy becomes;
Here the gain in incompleteness term is taken as β (1−ρ R ) which takes values in [1, β] when β > 1. This class of exponential entropy functions possesses various desirable properties which are not present in the usual Shanon's entropy as illustrated by Pal and Pal [35] . Some of them are as follows:
1. In Shanon's entropy the gain in information log(1/p) → ∞ as p → 0 and is undefined for p = 0. However, in real life, gain in information from an event, whether highly unlikely or highly probable is expected to be finite. However, exponential gain function ensures that such gain in information is bounded between 1 and β. 2. Logarithmic entropy is very sensitive to outliers due to the nature of log function. In contrast, exponential entropy is more robust to outliers. Particularly, a small probability relative to the other probabilities will bias the entropy towards the small probability event since the weight function log(1/p) in logarithmic entropy has an unbounded derivative. In comparison, the weight function β (1−p) used in exponential entropy has bounded derivative.
We have considered β = e in the subsequent sections, but any value greater than 1 is suitable.
In terms of mathematical setup, we consider {y 1 , y 2 , . . . y T } as the time series data with y t ∈ R p for any t = 1, 2, . . . T . With the number of changepoints equal to 1, we assume that the data comes from a distribution F at first and then gradually comes from a different distribution G after some time. Since the location of the changepoint is ambiguous in nature, this creates the possibility of splitting the set of time points U = {1, 2, ..., T } into two fuzzy partitions, γ F = U, µ F and γ G = U, µ G . Here, µ F and µ G denote the membership function of the respective partitions with observations coming from F and G respectively. Clearly, an obvious restriction is that µ F (t) + µ G (t) = 1. This is related to the incompleteness in knowledge about U and can be quantified by the entropy measures described by Sen and Pal [25] .
To formalize this notion of fuzziness, for t ∈ U, for an estimated changepoint s and bandwidth ∆, we define a fuzzy measure similar to that in [25] .
On the basis of this estimated changepoint s, the partitions γ F and γ G can be reformalized as γ s = γ F = {(t, µ s,∆ (t))|t ∈ U} and γ C s = γ G = {(t, 1 − µ s,∆ (t))|t ∈ U}, which captures the fuzzy nature of the two partitions due to the estimation of changepoint by a fixed quantity s.
Following the footsteps of grayness ambiguity, as formulated in [25] , we consider a tolerance function S w (u, v) such that, 1. S w (u, u) = 1 for any u ∈ U = {1, 2, . . . T }. can also be obtained. While these approximations can be pre-computed without the knowledge of time series data, we incorporate the knowledge of the available data by means of a regularity measure and combine it with the set approximations to obtain an ambiguity measure.
In order to create a regularity measure, for each timepoint t, a two sample test statistic is computed to detect the changes in the samples {y (t−δ+1) , y (t−δ+2) , . . . y t } and {y (t+1) , y (t+2) , . . . y (t+δ) }. As the regularity measure R(t) is expected to take higher value when there is no change in distribution and take lower value when there is a detected change in distribution, some transformation of test statistic could be used. One such regularity measure to detect the changes in mean could be based on Hotelling's T 2 test statistic.
A roughness measure is created for the two fuzzy partitions γ s and γ C s , incorporating the information of changes in the data as provided by suitably transformed test statistic R(t), and combining them with the respective upper and lower approximations of the fuzzy membership functions.
Based on these roughness measures, entropy quantifying the ambiguity for the fuzzy partitions of the time span for the specifically chosen changepoint s can be expressed using eqn. 6 or eqn. 7, with ρ R (X) replaced by the roughness measure given in eqn. 9. Thus, we obtain
Proceeding in the direction of any general changepoint detection method [26] for detecting a single changepoint present in the data, any CPD algorithm can be broadly expressed as
where t * is the estimated changepoint and V (·, ·) is a cost function as shown in eqn. 2, which can be interpreted as a regularity measure. While in this way, the regularity measure R(t) itself becomes an indicator of the changepoint, it can be greatly enhanced in combination of fuzzy and rough set theory, by constructing the entropy as given in eqn. 10 . Thus, according to the proposed algorithm, the estimated changepoint is given as
This estimated changepoint t * shown in eqn. 12 may be denoted as the rough-fuzzy CP, and the method may be called rough-fuzzy CPD. Note that while the global minima serves as the estimate of single changepoint, the local minima of the entropy function, after suitable testing, are used for multiple changepoint detection.
The reason for including roughness in the proposed changepoint detection algorithm is subtle. Since the underlying distributions of the time series data is unknown, all the information about the changepoint must be gathered in terms of the available observations y t , which is now summarized only through a single attribute R(t), the regularity measure.
Along with this loss of information, since the regularity measure R(t) is computed based on overlapping windows, any information about the locality of a timepoint will permeate to its neighbouring timepoints as well, resulting in incomplete information about the timepoints itself. This rough resemblance between different time points is modelled by the tolerance relation which eventually leads to the rough set formulation.
Further, the entropy function depends on the hyper parameters w, δ and ∆. In the proposed rough-fuzzy CPD, w denotes the degree of roughness of the tolerance function with higher values indicating greater roughness and ∆ determines the fuzziness of the membership function with higher values corresponding to greater fuzziness. It is important to choose the values of w and ∆ correctly as wrongly chosen values might result in relatively higher error in estimation, though a wide range of such optimal values are available as shown in section 5.6.
Here we provide two mathematical properties of the aforesaid estimate t * , involving rough-fuzzy entropy, towards the detection of gradual changepoints. First one deals with the issue of its speedy computation, while the other theoretically establishes its ability in detecting multiple changepoints. In the section 4.1, we obtain exact solutions of upper and lower approximations and also derive relations between them to reduce computation. In section 4.2, we prove a theorem which gives the asymptotic distribution of rough-fuzzy entropy under the null hypothesis that there are no changepoints. An immediate corollary of this theorem gives us the joint asymptotic distribution of rough-fuzzy entropy evaluated at multiple proposed changepoints under the null hypothesis. This helps to present our algorithm in a hypothesis testing framework and also test for false positives.
We start by introducing a result which relates the upper and lower approximations of the set γ s with the approximations for the complement set γ C s . The significance of the result is that in order to obtain the roughness measure ρ ∆,δ,w (γ s ) and ρ ∆,δ,w (γ C s ) as given in eqn. 9, it is enough to focus the computation on only one of these terms, and the other can be obtained as a byproduct of the result. Lemma 1.
Proof. Starting with the lower approximation;
The other equality proceeds in an exactly similar way.
Also, since the computation of the lower and upper approximations M γs (t) and M γs (t) is independent of the data, it can be pre-computed for the changepoint analysis problems, given the knowledge of the number of timepoints T . However, if T is large, computation of eqn. 5 poses a high memory and computational complexity. However, it is possible to obtain exact expressions of these lower and upper approximations under a very general setup, which greatly reduces both the computational and storage cost complexities of the whole process.
To establish an explicit formula for lower and upper approximations, we first consider the situation when M γ
which happens if and only if there exists a ψ ∈ U such that, both S w (t, ψ) and µ s,∆ (ψ) are equal to 0. While the complementary tolerance function S w is 0, if and only if S w is 1, i.e. the two arguments satisfy t = ψ. On the other hand, µ s,∆ (ψ) = 0 if and only if ψ ≥ (s + ∆), combining this with t = ψ yields, t ≤ (s + ∆).
On the other extreme, M γ s (t) = 1, if and only if, either S w (t, ψ) = 0 or µ ∆,s (ψ) = 1. The former happens when |t − ψ| ≥ 2w, and the latter happens if ψ ≤ (s − ∆). Thus, for any t ≤ (s − 2w − ∆), M γ s (t) = 1. To see the behaviour of M γ
as a minimizer of the function max S w (t, ψ), µ s,∆ (ψ) with respect to ψ. This is shown by the upper envelope curve of S w (t, ψ) and µ s,∆ (ψ) in figure 1 . Thus, it is easy to see that the minimizer would appear at a point t * where the fuzzy membership function and the tolerance function crosses each other, i.e., S w (t, t * ) = µ s,∆ (t * ). This leads us to a result depicting a much easier way to compute the lower and upper approximations. 
It is possible to obtain a closed form solution of S w (t, t * ) = µ s,∆ (t * ) and similar equations for some specific tolerance functions. One such specific choice is provided in the following corollary.
Corollary 1. Let the membership function µ s,∆ (t) be given as in eqn. 8, and a tolerance function be given as
Then the lower and upper approximations of the left and right partitions for a chosen changepoint s can be obtained as
while the approximations for the complementary set can be obtained using lemma 1. Lemma 2. If the tolerance function S w (u, v) can be expressed as a function of the absolute difference of its arguments, i.e. S w (u, v) = g(|u − v|) such that g(·) is symmetric about 0, then;
Proof. We can rewrite theorem 1, as follows,
To prove this lemma, we consider the 3 cases separately. 
So, lemma 2 also holds for case 3, proving that the result is true in general.
The significance of lemma 2 is that, in case of symmetric and location invariant tolerance function, only one, either lower or upper, approximation curve is required to be computed.
While the rough-fuzzy CPD can be employed and a single changepoint can be detected using eqn. 12, multiple changepoints can be detected by local minima of the curve H E ∆,δ,w (s) as a function of s. However, a reference curve must be computed in order to select the true changepoints from many local minimas. In statistical language, these reference curve is usually computed based on the distribution of the statistic under a suitably chosen null hypothesis, by modifying the problem into a hypothesis testing framework.
Considering the mathematical framework given in eqn. 1, we can formulate the problem of detecting changepoint as a hypothesis testing problem.
There is at least one inequality Since the regularity measure R(t) is an indicator of a possible changepoint, hence under the null hypothesis H 0 ; E(R(t)) is a constant independent of the time t. On the basis of this, we obtain asymptotic null distribution of the proposed statistic under some reasonable assumptions on the asymptotic null distribution of the regularity measure. Clearly, the asymptotics follow when the number of samples for constructing R(t) i.e. δ is tended to infinity, which forces the total number of timepoints T to be tended to infinity as well. Thus, in order to talk about asymptotics, we restrict ourselves to an infinite dimensional normed space, which without any loss of generality, can be taken as l ∞ (N), the set of all uniformly bounded functions from N to R. Also, while the number of timepoints T increases to infinity, the intervals between successive observations tend to zero, in order to make the total period of observation constant pertaining to most of the practical situations.
Let us denote the regularity measure R δn as a l ∞ (N) valued random element defined on some probability measure space (Ω, A, P) such that R δn (t) is a R-valued random variable denoting the regularity measure based on the 2δ n subsamples centered at t for each t = 1, 2, . . . ∞. Theorem 2. Assume that under the null hypothesis that there is no changepoint, the regularity measure R δn has an asymptotic distribution such that for some sequence a δn → ∞, a δn (R δn − µ1(·)) Z where 1 ∈ l ∞ (N) is the identity function 1(x) = x, Z is an infinite dimensional Gaussian process with mean function identically equal to 0 and covariance function σ : N × N → [0, ∞).
Also, assume the following regularity conditions:
2. For the proposed changepoint s, the series ∞ t=1 M γs (t) and
Then, the exponential entropy based statistic H E ∆,δn,w (s) defined in eqn. 10 has an asymptotic distribution with the same normalizing constant, a δn such that as a δn → ∞,
)e b(s) and Z * is a univariate normally distributed random variable with mean 0 and variance σ * where;
and;
provided that the series expression of σ * is convergent.
Proof. We first fix the parameters ∆, w and fix the proposed changepoint s. Let us define, ρ ∆,δn,w (γ s ) as a function from l ∞ (N) to R, denoted by ρ (s) ;
We would first compute the Fréchet derivative of this at µ1(·).
where | · | represents the usual absolute value norm on real numbers. Now, since µ = 0, without loss of generality assume that, µ > > 0 for some small non-negative . With h ∞ → 0, we can thus make,
for sufficiently small h ∞ and some finite real number B. On the other hand,
since, M γs (t) ≤ M γs (t) and both are nonnegative
To see that the derivative given in eqn. 15 is well defined, note that
which shows that the infinite series is convergent as h ∈ l ∞ (N).
This shows that the function ρ (s) given in eqn. 14 is Fréchet differentiable (see Miranda and Fichmann [36] for definition) at µ1 and the derivative is given by eqn. 15 . In a very similar way, the roughness measure corresponding to the completement set γ C s can also be shown to be Fréchet differentiable at µ1 as a function of {R(t) : t = 0, 1, 2, . . . }. Now, in order to obtain the exponential entropy as shown in eqn. 10, we consider the function g : [0, 1] 2 → R defined as;
g(x, y) = xe (1−x) + ye (1−y)
Clearly, g is Fréchet differentiable at every point of the domain. Finally, a chain rule [37, 38] can be applied on the composition of g and the roughness measures to show that, Ψ s is Fréchet differentiable at µ1 where Ψ s : l ∞ (N) → R is such that Ψ s (R δn ) = H E ∆,δn,w (s). Since Fréchet differentiablity implies Hadamard differentiability (see discussion followed by Definition 2.1 of Shao [39] ), and outputs a linear operator as the derivative, Ψ s is also Hadamard differentiable with the derivative given by;
where the function A(n) is as given in eqn. 13. Clearly, this is a linear and continuous map.
The proof of the result now follows from an infinite dimensional generalization of delta method (Theorem 1 and Section 1.4 of Römisch [40] ). Based on the discussion above, we see that due to the assumption, Z is a gaussian process, and also the hadamard derivative of the function Ψ s is linear. Thus, Ψ s (Z) is a normally distributed random variable with the mean 0 and variance σ * , as given in the statement of the theorem.
An immediate extension of theorem 2 is the analogus result for multiple proposed changepoints.
Corollary 2. Under the same assumptions and conditions of theorem 2, the vector of exponential entropy based statistic H E ∆,δn,w (s) for multiple proposed changepoints s 1 , s 2 , . . . s k has the following asymptotic distribution as a δn → ∞;
where 0 k is the k dimensional null vector, and the entries of the k × k dispersion matrix are given as;
On the basis of theorem 2 and theorem 2, a hypothesis testing framework can be laid out to detect the significant changepoints. The final algorithm is shown in algorithm 1. Another consequence of theorem 2 is that if the parameters w and ∆ are carefully chosen so that the obtained local minima of the entropy function H E ∆,δ,n (s) are separated by atleast (4w + 2∆), then the entropy corresponding to these functions are asymptotically independent. Thus, for such well-separated changepoints, each of them can be tested individually for false positives.
In order to asses a comparative study of the proposed rough-fuzzy improvement over any changepoint detection algorithm, some simulations are performed. We consider a general model of the observations as follows:
where µ(t) is the mean function dependent on time, while t denotes independent and identically distributed random variables, each distributed according to a standard normal distribution. We consider 3 different types of mean curves as shown in figure 2 . Compute regularity measure R(t) as a similarity between S 1t and S 2t ; end for s = 1 to T do for t = 1 to T do Use theorem 1 to compute M γs (t); Use lemma 2 to compute M γs (t); Use lemma 1 to compute M γ C s (t) and M γ C s (t); end Compute roughness measure ρ ∆,δ,w (γ s ) and ρ ∆,δ,w (γ C s ) using eqn. 9; Compute exponential entropy H E ∆,δ,w (s) using eqn. 10; end Find local minimas of the sequence {H E ∆,δ,w (s) : s = 1, 2, . . . T };
One can output these local minimas as proposed changepoints, however if theoretical expressions for µ and σ as denoted in theorem 2 for the underlying regularity measure R(t) is available, the following testing framework can be used to further enhance its ability for each local minima s k of H E ∆,δ,w do Compute H * (s k ) as given in theorem 2 by replacing the infinite sums as sums from t = 1 to T ; for n = 1 to T do Use eqn. 13 to compute A s k (n) by replacing the infinite sums as sums from t = 1 to T ; end Compute σ * for the changepoint s k as given in theorem 2 using finite sums from t = 1 to T ; Accept s k as a changepoint if a δ H E ∆,δ,w (s k ) − H * (s k ) / √ σ * < z α , where z α is the α-th quantile of the standard normal distribution; end Figure 2a depicts the scenario S1 of abrupt change, which is the popular definition of a changepoint. Most of the existing algorithms are able to detect this type of change. Figure 2b shows the scenario S2 where the mean curve is piecewise linear, and hence a test for changes in slope of the regression would be able to detect such changes. While this shows a situation where the change in slope of the regression equation has a discrete jump, situation S3 indicates a continual change in the slope thus incorporating a much smoother and gradual jump in the dataset as shown in subfigure 2c.
In order to establish the comparison, we consider three test statistic that are well used in a two-sample setup to detect the changes in mean. The parametric statistic as square of two sample t-test statistic, nonparametric Kolmogorov Smirnov test statistic and Augmented Dickey Fuller test statistic among unit root tests were chosen. In each case, the regularity measure R(t) is obtained by taking reciprocal of the test statistics, to make sure R(t) has a higher value in absence of changepoints and has a lower value in presence of changepoint. Both eqn. 11 and eqn. 12 were used to obtain the estimates of the changepoints, and the process was repeated for 200 Monte Carlo resamples and used to obtain error measures. In applying rough-fuzzy CPD, δ = 50 was chosen to compute the regularity measure, while different values of w and ∆ were used in the experiment to see their effects.
We present the results of simulation studies for all the 3 cases described above. All the following simulations and real data applications have been performed by the python package roufcp developed by us. Table 1 summarizes the result for all 3 types of mean shift changepoints described above. We begin analyzing the results with changepoint of type S1, where a discrete jump in the mean function has occurred. Note that, the hyperpa- Overall, for discrete jump type changepoints rough-fuzzy CPD performs poorly, giving higher MSE than base model for t-test and Kolmogorov Smirnov tests, and higher values of w and ∆ will decrease the accuracy in prediction. This is because the fundamental assumption of the proposed method viz. "change is fuzzy in nature and not abrupt" is violated in this case. Now, we look at the other 2 types of changepoints where the change is not abrupt and occurs gradually over a period of time.
From Table 1 we observe the performance of rough-fuzzy CPD for situation S2 with continuous change in mean function. For both t-test statistic and Kolmogorov-Smirnov statistic, the RMSE is much lower in rough-fuzzy CPD relative to the base method based on the regularity measure R(t). However, with an increase in w and ∆, the RMSE reduces first and then increases, possibly suggesting an existence of optimal hyperparameters in between. Because of the specific parametric setup, Augmented Dickey Fuller test and nonparametric Kolmogorov Smirnov test generally perform worse than parametric t-test. However, in practical applications when the data generating processes are not known, ADF and KS test might perform better in conjunction with our proposed improvement. An interesting phenomenon occurs when we use Augmented Dickey Fuller test statistic based regularity measure. As shown in figure 3 , the ADF test usually outputs two changepoints around the true changepoint at 666; but as the fuzziness is incorporated in the model by increasing w and ∆, the bimodal distribution gradually becomes unimodal. However, there possibly remains a negative bias in estimation, as indicated by the mode of the density curve of the rough-fuzzy CPD for w = ∆ = 50.
Turning to situation S3 with smooth change in mean function, the results are found to be similar to scenario S2. Here also rough-fuzzy CPD has reduced the MSE by more than 50% in most cases. However, we see that for w = ∆ = 100 the efficiency of model is greatly reduced and it even performs slightly worse than base t-test. Again, as mentioned earlier, it is of utmost importance to choose the values of the hyperparameters w and ∆ carefully to obtain accurate estimates of the changepoint even in the situations where the mean function is gradual and the underlying assumption of the method is not violated. However, as shown by table 1, rough-fuzzy CPD generally obtains higher reduction in MSE for S2 (continuous jump) and S3 (smooth jump) compared to S1 (discrete jump). So, with greater degree of ambiguity in changepoint, the relative performance of our model increases as expected. We shall illustrate this phenomenon in greater details and more rigorously in subsection 5.4.
While detecting fuzzy changepoints is a hard problem, doing so in a noisy data with low Signal-to-Noise Ratio (SNR) is even harder. Here signal-to-noise ratio is defined as
where N is the noise component of the data and S is the true signal component of the data, which mainly comprises of the mean function in the time series observations. To check the performance of rough-fuzzy CPD under different SNR's, we keep noise N same as before with standard normally distributed and vary the value of signal S by changing the size of the jump in the mean function. In this simulation setup, we consider scenario S2 with continuous change in mean function, with the jump sizes S == 1/5, 1/4, 1/3, 1/2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 . For each such setup, monte carlo estimates of the MSE of the estimated changepoints by the proposed method rough-fuzzy CPD and the base method using Kolmogorov-Smirnov test, are calculated based on 1000 resamples. In each resample, the true changepoint value is kept fixed at 666. Figure 4 shows the variation of RMSE of estimated changepoint for the proposed rough-fuzzy CPD as well as the base method. We observe that rough-fuzzy CPD has obtained lower RMSE than the base method for all values of SNR. While SNR increases, the predictive capabilities of both the methods increase. However, it is important to note that while the base method performs very badly for lower values of SNR (< 1), the performance of rough-fuzzy CPD using the same regularity measure is not so severely affected. In fact throughout the low and high values of SNR, the relative improvement of MSE achieved by rough-fuzzy CPD remains fairly uniform ranging from 88 − 97%, with the highest reduction achieved when SNR is 2 or 3.
The main aim of this paper is to provide a method of changepoint detection when the change is gradual and not abrupt. In our earlier simulations, scenario S2 and S3 depict such situations where such a gradual change has occurred.
To check how rough-fuzzy CPD performs under different levels of graduality or "fuzziness" of change, we consider different cases of scenario S2 where the mean function is defined as follows;
where s 0 is the true changepoint and the parameter F defines the degree of graduality or "fuzziness" of the changepoint. To see the effect of F on the estimate of rough-fuzzy CPD, we consider 1000 resamples of scenario S2 with true changepoint at s 0 = 666, for 15 different values of F ranging from 10 to 150. The MSE for each level of fuzziness F is calculated using a monte-carlo method, for the estimated changepoint obtained by rough-fuzzy CPD as well as the underlying base method with Kolmogorov-Smirnov statistic. Figure 5 shows the RMSE of estimated changepoints by rough-fuzzy CPD and its base counterpart for different values of fuzziness F, along with the relative decrease obtained by rough-fuzzy CPD. As shown in subfigure 5a, higher degree of fuzziness leads to higher error in estimation for both methods, though rough-fuzzy CPD is not as severely affected as the base method. Figure 5b depicts that the relative performance gain by the proposed rough-fuzzy improvement is only possible when the fuzziness in the true changepoint crosses a certain threshold, about F = 31 for our specific setup. However, as the fuzziness increases, due to extremely sensitive performance of the base method, rough-fuzzy CPD could achieves nearly 95 − 99% performance gain in terms of MSE. 
To compare performance of the rough-fuzzy CPD with existing changepoint detection algorithms, we have chosen three existing methods of fuzzy changepoint detection, namely FCP algorithm [16] for regression models, Fuzzy shift changepoint (FSCP) algorithm [41] and Fuzzy classification maximum likelihood changepoint (FCMLCP) algorithm [42] . Complementary to that, we also consider 3 state of the art abrupt changepoint detection algorithms, namely Wild Binary Segmentation [43] , Pruned Exact Linear Time (PELT) [7] and Bayesian Online changepoint Detection [9, 10] . To compare the performances between rough-fuzzy CPD and these existing methods, we consider rough-fuzzy CPD with "best" tuning parameter in combination with Kolmogorov-Smirnov statistic, which should perform equally well under any general model due to its nonparametric nature. Table 2 : Performance comparison of rough-fuzzy CPD (proposed) with existing fuzzy changepoint detection methods under the simulation setups. Best method in each setup is indicated by * . Table 2 shows the root mean squared error (RMSE) of the estimated changepoints by the proposed algorithm roughfuzzy CPD and the other existing changepoint detection methods, for the simulation scenarios described in section 5.1, with the best model indicated for each scenario. As shown in table 2, in the scenario S2 and S3 where fuzziness about the changepoint is present, the proposed model outperforms the existing methods. However, in case of abrupt changepoints, PELT, Wild Binary Segmentation and FCP are more suited to the task. The Bayesian Online changepoint detection method turns out to be very close to our method in terms of performance for smooth change in mean, but at the cost of inaccurate estimate for abrupt changes. One possible reason for this phenomenon could be the underlying nature of BOCD, which being an online changepoint detection algorithm, determines presence of a changepoint only from the past data. In such situation, once a true changepoint is visited, BOCD needs to see sufficient samples from the new distributions before it can conclude such a changepoint is present, thus creating a lag between the true changepoint and the estimated changepoint. This inability in abrupt changepoint becomes beneficial in detecting gradual changepoint for BOCD.
The proposed method rough-fuzzy CPD depends on 3 main factors viz. the regularity measure R(t), the tunable hyperparameters w and ∆. w denotes the degree of roughness of the tolerance function with higher values indicating greater roughness. Similarly, the parameter ∆ determines the fuzziness of the membership function with higher values corresponding to greater fuzziness. While we do not have control over the regularity measure R(t) as it is exogenous to rough-fuzzy CPD, we need to choose the values of w and ∆ judiciously to obtain better estimates. To understand the effect of parameters w and ∆ we perform some sensitivity analysis. We consider 2 scenarios to The difference in the range of values of RMSE between subfigures 6a and 6b can be attributed to the difference in the mean curve of the data used in the 2 scenarios. As shown by using a shared color scale, rough-fuzzy CPD is able to achieve a lower RMSE for situation S3 than in situation S2 with their corresponding optimal hyperparameter values. One possible reason could be the resemblance of the chosen membership function for rough-fuzzy CPD with the change in the mean function for situation S3.
We consider three real data sets to show the performance of our method rough-fuzzy CPD over the usual regularity measure based methods. Two popular datasets in changepoint analysis, namely "Flow of the River Nile" data and "Seatbelts" data regarding monthly Road Casualties in Great Britain 1969−84, collected from datasets package [44] in R [45] . For the third illustration, changepoints are estimated from time varying reproduction rate from coronavirus disease of 2019 (COVID-19) incidence dataset.
The very well-known "Flow of the river Nile" dataset comprises measurements of the annual flow of Nile river at Aswan from 1871 to 1970. The data has a possible changepoint near 1898 which is associated with the Fashoda incident [46] in the same year. The results for Nile dataset is shown in figure 7 , where we use t-test as a regularity measure. As shown in subfigure 7a, there are multiple minimas detected by the base method, while the proposed rough-fuzzy improvement smooths out these minimas and the false positives are automatically removed. The estimated changepoint by our rough-fuzzy CPD turns out to be at 1902, which coincides with the completion of Zifta Barrage and Assiut Barrage [47] and is close to the commonly believed changepoint at 1898.
The popular "Seatbelts" dataset consists of the number of drivers in Great Britain wearing seatbelts during the time period Jan, 1969 to Dec, 1984. There are possibly two evident changepoints present in the data, first one corresponds to the start of seat belt legislation movements from 1972 when the seatbelts were enforced compulsory in newly manufactured cars, and the second one corresponding to the compulsory enforcement of seat belt wearing while driving in 1983 [15] . Figure 8 explains the performance of rough-fuzzy CPD in detecting changepoints for Seatbelts data. Similar to Nile data, the regularity measure based on nonparametric Kolmogorv Smirnov test tends to have too many local minimas, each of which can be possibly thought of as an estimate of changepoint. However, in conjunction with the rough-fuzzy improvement, the prominent local minima appear in 1975 and 1983, both of which are in close proximity of the true changepoints. Such work has been done for some countries like USA [49] and Germany [50] . To identify changepoints, Dehning et al. [50] calculated the time varying rate of transmission β(t) in the usual Susceptible Infected Recovered (SIR) model [51] and detected changepoints in β(t) through the course of the pandemic. For the SIR model, the transmission rate is defined as the rate of transmission of infection from an infected individual to a susceptible individual [51] .
While the SIR model and its parameters are relatively straightforward to understand and easy to estimate, the dynamics of transmission of COVID-19 is much more complex due to the presence of factors like undetected asymptomatic transmissions, latency period, incubation period, delay in reporting, under reporting, different transmission rates for asymptomatic and symptomatic individuals, quarantined and hospitalized individuals, and erroneous testing resulting in false negatives, to name a few. So, β(t) alone might not be a suitable representative of the spread of the disease.
So, we look at another related parameter which is the basic reproduction number R 0 . While SIR model assumes constant R 0 we estimate the time varying reproduction number R t . R t can be defined as the expected number of cases directly generated by one case in the population at time t [52] . We estimate R t using the EpiEstim package [53] in R based on the incidence curve from 15 th March to 25 th September in India. The required data on the number of confirmed cases of COVID-19 has been collected from an API made by volunteer driven covid19india group [54] . We then apply rough-fuzzy CPD on the estimated R t to identify the changepoints. Note that modelling the changepoints in a fuzzy manner, instead of crisp, is logical and appropriate here as interventions and measures rolled out by government are impossible to be implemented throughout a vast country like in India instantaneously. Figure 9 shows the estimated changepoints along with the estimates of R t . We see that our method detects 5 changepoints on 1 st April, 2 nd May, 16 th June, 1 st July and 4 th August. Of these, the changepoints on 2 nd May, 1 st July and 4 th August are very close to the date of commencement of Lockdown 3, Unlock 2 and Unlock 3 respectively. The first changepoint (on 1 st April) corresponds to the initial elbow in the estimate of R t after 7 days of the 1 st Lockdown in India. This shows that the effect of the 1 st Lockdown was not immediate and it took approximately 7 days to initially check the unrestricted spread of the disease. Hence, we infer that the effect of Lockdown 3, Unlock 2 and Unlock 3 were much more pronounced on R t than the other nationwide interventions in India.
Though gradual changepoints are present in various time series data, they are usually overlooked. Our study aims to contribute in this direction by enriching the existing methodologies using the principles of Fuzzy Rough set theory. In fact, the biggest strength of our approach is that it is independent of the base method, and thus any changepoint detection algorithm expressed as shown in figure 11 can be subjected to improvement by our proposal. Here, we have presented only 3 cases of base methods for computing the regularity measure -parametric two sample test, nonparametric two sample tests and stationarity test (unit root test). However, our choices are not limited to the Date R t Mean R t Predicted CP Figure 9 : Changepoints in estimated R t for India aforementioned cases. Any method for detecting changepoint which provides any type of anomaly score or regularity score can be used in our framework with suitable transformation. Thus our method allows to utilize the rich collection of methods existing for crisp changepoint detection for the problem of fuzzy changepoint detection.
As various simulations show, even for very simple regularity measures, like t-test statistic and Kolmogorov-Smirnov test statistic, combining them with the rough-fuzzy CPD increases their efficiency by far. In all cases, except for the discrete jump models (where the assumption of fuzziness in changepoint is violated), rough-fuzzy CPD reduces the MSE in detecting the changepoints. In comparison to existing fuzzy and crisp changepoint detection algorithms, rough-fuzzy CPD turns out to be more efficient in estimation of continuous and smooth changes in mean.
The asymptotic distribution mentioned in theorem 2 and theorem 2 connects the rough-fuzzy CPD to a hypothesis testing framework, allowing one to output the statistical significance of the estimated changepoints as well. Since, theorem 2 allows us to derive the joint asymptotic distribution of the proposed entropy at multiple time points, we are able to rule out false positives and apply the rough-fuzzy CPD for multiple changepoint detection problem.
From a computational point of view, this paper addresses the issue of obtaining closed form expressions for upper and lower approximations of the rough fuzzy partitions, described by an estimated changepoint, thus allowing the overall algorithm to be much faster. In general, the time and space complexity of the overall algorithm will be dominated by the cost of computing the regularity measures.
Our approach of using fuzzy and rough set theories has definite advantages which is reflected in the robustness of the rough-fuzzy CPD. Under changes in the signal-to-noise ratio, this method gives a steady improvement over the regularity measure based changepoint detection. We observe that for a diverse range of SNR, the improvement in accuracy of estimates obtained by the rough-fuzzy CPD over that of the base model remains fairly uniform ranging from 88% to 96%. On the other hand, increasing the fuzziness F in the continuous change in mean function increases the efficiency of the rough-fuzzy CPD in comparison to the base statistic used. As fuzziness increases, a rapid increment in efficiency can be observed. Also, rough-fuzzy CPD rarely outputs outlying or spurious false positive estimate of changepoint, precisely because the entropy curve H E ∆,δ,w (s) possesses more smoothness properties than its regularity measure R(t) counterpart. In fact, even in the cases when the base model predicts a bimodal distribution of estimated changepoints, our model shrinks the two modes towards the true changepoint, as illustrated in figure 3 . Finally, through sensitivity analysis, we have shown that the rough-fuzzy CPD performs reasonably well on a wide range of hyperparameter values.
On the flip side, there are some obvious limitations. The rough-fuzzy CPD may not perform good if the assumption of fuzziness in the true changepoint is violated (i.e., there is a discrete jump discontinuity in the mean function), or if the regularity measure R(t) is a bad indicator for the type of changepoint that we are trying to detect. Another limitation could be the choice of the hyperparameters w, ∆ and δ.
A future possibility for extension of this investigation may consider building a probabilistic view of the detected changepoint, which should enable one to provide a confidence interval for the estimate of changepoint, which is more meaningful in terms of gradual change. Further, rough-fuzzy CPD can be viewed as an extension of an image segmentation algorithm [25] into the domain of changepoint detection. A future endeavor in this direction could be to generalize a family of image segmentation algorithms to fit perfectly in the context of a changepoint detection problem.
For broader dissemination of our work, we have developed the python package roufcp which is available at pypi.org/project/roufcp/. All codes for the package has been open sourced and are made available at a github respository github.com/subroy13/roufcp.