Research article Special Issues

CATL's stock price forecasting and its derived option pricing: a novel extended fNSDE-net method

  • Received: 28 November 2024 Revised: 18 January 2025 Accepted: 05 February 2025 Published: 11 February 2025
  • MSC : 62M45, 62P05, 91G80

  • This paper presents a novel numerical method, named extended fractional neural stochastic differential equation (fNSDE)-Net, which combines the generative adversarial network (GAN) and fNSDE with a self-attention module. The method is designed to generate and forecast the stock price of Contemporary Amperex Technology Co., Ltd. (CATL) in China. The primary challenge of this study lies in the fact that the input consists of a single, irregular time-series dataset with long-range dependencies (i.e., Hurst index H>12), and its inherent noise cannot be directly modeled using pure Brownian motion. The proposed method not only generates multiple sample paths based on the initial data in a probabilistic sense but also preserves the long-term memory characteristics of the generated samples. Moreover, the pricing of a Bermuda call option, induced by stock prices, is explored. Through a series of numerical error comparisons and estimator reliability tests, the proposed method outperforms both the pure fNSDE-GAN method and the NSDE-GAN method in terms of fitting and generalization performance, thereby demonstrating its effectiveness.

    Citation: Xiao Qi, Tianyao Duan, Lihua Wang, Huan Guo. CATL's stock price forecasting and its derived option pricing: a novel extended fNSDE-net method[J]. AIMS Mathematics, 2025, 10(2): 2444-2465. doi: 10.3934/math.2025114

    Related Papers:

    [1] Tahair Rasham, Muhammad Nazam, Hassen Aydi, Abdullah Shoaib, Choonkil Park, Jung Rye Lee . Hybrid pair of multivalued mappings in modular-like metric spaces and applications. AIMS Mathematics, 2022, 7(6): 10582-10595. doi: 10.3934/math.2022590
    [2] Ahlam Almulhim . Signed double Italian domination. AIMS Mathematics, 2023, 8(12): 30895-30909. doi: 10.3934/math.20231580
    [3] Abel Cabrera Martínez, Iztok Peterin, Ismael G. Yero . Roman domination in direct product graphs and rooted product graphs. AIMS Mathematics, 2021, 6(10): 11084-11096. doi: 10.3934/math.2021643
    [4] Ana Klobučar Barišić, Antoaneta Klobučar . Double total domination number in certain chemical graphs. AIMS Mathematics, 2022, 7(11): 19629-19640. doi: 10.3934/math.20221076
    [5] Linyu Li, Jun Yue, Xia Zhang . Double total domination number of Cartesian product of paths. AIMS Mathematics, 2023, 8(4): 9506-9519. doi: 10.3934/math.2023479
    [6] Bana Al Subaiei, Ahlam AlMulhim, Abolape Deborah Akwu . Vertex-edge perfect Roman domination number. AIMS Mathematics, 2023, 8(9): 21472-21483. doi: 10.3934/math.20231094
    [7] S. Gajavalli, A. Berin Greeni . On strong geodeticity in the lexicographic product of graphs. AIMS Mathematics, 2024, 9(8): 20367-20389. doi: 10.3934/math.2024991
    [8] Mingyu Zhang, Junxia Zhang . On Roman balanced domination of graphs. AIMS Mathematics, 2024, 9(12): 36001-36011. doi: 10.3934/math.20241707
    [9] Chang Liu, Jianping Li . Sharp bounds on the zeroth-order general Randić index of trees in terms of domination number. AIMS Mathematics, 2022, 7(2): 2529-2542. doi: 10.3934/math.2022142
    [10] Abdullah Shoaib, Tahair Rasham, Giuseppe Marino, Jung Rye Lee, Choonkil Park . Fixed point results for dominated mappings in rectangular b-metric spaces with applications. AIMS Mathematics, 2020, 5(5): 5221-5229. doi: 10.3934/math.2020335
  • This paper presents a novel numerical method, named extended fractional neural stochastic differential equation (fNSDE)-Net, which combines the generative adversarial network (GAN) and fNSDE with a self-attention module. The method is designed to generate and forecast the stock price of Contemporary Amperex Technology Co., Ltd. (CATL) in China. The primary challenge of this study lies in the fact that the input consists of a single, irregular time-series dataset with long-range dependencies (i.e., Hurst index H>12), and its inherent noise cannot be directly modeled using pure Brownian motion. The proposed method not only generates multiple sample paths based on the initial data in a probabilistic sense but also preserves the long-term memory characteristics of the generated samples. Moreover, the pricing of a Bermuda call option, induced by stock prices, is explored. Through a series of numerical error comparisons and estimator reliability tests, the proposed method outperforms both the pure fNSDE-GAN method and the NSDE-GAN method in terms of fitting and generalization performance, thereby demonstrating its effectiveness.



    Throughout this paper, all graphs considered are finite, undirected, loopless and without multiple edges. We refer the reader to [3,18] for terminology and notation in graph theory.

    Let G=(V,E) be a graph of order n with vertex set V(G) and edge set E(G). The open neighborhood of a vertex v in G is NG(v)=N(v)={uV(G)|uvE(G)}, and the closed neighborhood of v is NG[v]=N[v]=N(v){v}. The degree of a vertex v in the graph G is dG(v)=d(v)=|N(v)|. Let δ(G)=δ and Δ(G)=Δ denote the minimum and maximum degree of a graph G, respectively. Denote by G[H] the induced subgraph of G induced by H with HV(G). A vertex v in G is a leaf if d(v)=1. A vertex u is a support vertex if u has a leaf neighbor. Denote L(u)={v|uvE(G),d(v)=1}.

    A subset ME(G) is called a matching in G if no two elements are adjacent in G. A vertex v is said to be M-saturated if some edges of M are incident with v, otherwise, v is M-unsaturated. If every vertex of G is M-saturated, the matching M is perfect. M is a maximum matching if G has no matching M with |M|>|M|. Let R be a subgraph of G, M be a matching of G, vV(R), uvM. We say the vertex v is RI (resp. RO) if uV(R) (resp. uV(R)).

    A set VC of vertices in a graph G is a vertex cover of G if all the edges are touched by the vertices in VC. A vertex cover VC of G is minimal if no proper subset of it is a vertex cover of G. A minimal vertex cover of maximum cardinality is called a VC-set. In 2001, Mishra et al. [13] denote by MAX-MIN-VC the problem of finding a VC-set of G. Bazgan et al. [2] showed that MAX-MIN-VC is APX-complete for cubic graphs.

    A set PDV(G) in a graph G is a paired dominating set if every vertex vPD is adjacent to a vertex in PD and the subgraph induced by PD contains a perfect matching. Paired domination was proposed in 1996 [9] and was studied for example in [4,5,6,12,16,17]. A paired dominating set PD of G is minimal if there is no proper subset PDPD which is a paired dominating set of G. A minimal paired dominating set with maximum cardinality is called a Γpr(G)-set. The upper paired domination number of G is the cardinality of a Γpr(G)-set of G. Denote by Upper-PDS the problem of finding a Γpr(G)-set of G. Upper paired domination was introduced by Dorbec et al. in [7]. They investigated the relationship between the upper total domination and upper paired domination numbers of a graph. Later, they established bounds on upper paired domination number for connected claw-free graphs[10]. Denote Pr(v)={u|uPD,N(u)PD={v},uvE(G)}, where PD is a minimal paired dominating set of G.

    Recently, Michael et al. showed that Upper-PDS is NP-hard for split graphs and bipartite graphs, and APX-completeness of Upper-PDS for bipartite graphs with Δ=4 in [11]. In order to improve the results in [11], we show that Upper-PDS is APX-complete for bipartite graphs with Δ=3.

    The class APX is the set of NP-optimization problems that allow polynomial-time approximation algorithms with approximation ratio bounded by a constant.

    First, we recall the notation of L-reduction [1,15]. Given two NP-optimization problems H and G and polynomial time transformation f from instances of H to instances of G, we say that f is an L-reduction if there are positive constants α and β such that for every instance x of H:

    (i) optG(f(x))αoptH(x);

    (ii) for every feasible solution y of f(x) with objective value mG(f(x),y)=a, we can find a solution y of x with mH(x,y)=b in polynomial time such that |optH(x)b|β|optG(f(x))a|.

    To show that a problem PAPX is APX-complete, it's enough to show that there is an L-reduction from some APX-complete problems to P.

    Denote by MAX-MIN-VC the problem of finding a maximum minimal vertex cover of G. Note that, Minimum Domination problem is APX-complete even for bipartite graphs with maximum degree 3 [14], and Minimum Independent Domination problem [8] is the complement problem of MAX-MIN-VC in a graph G. We can obtain an L-reduction from Minimum Domination problem to Minimum Independent Domination problem by replacing every edge uv with a path Puv=uabcv with α=7, β=1. It's clear that Minimum Independent Domination problem and MAX-MIN-VC are in APX. Thus, Minimum Independent Domination problem is APX-complete even for bipartite graphs with maximum degree 3, so is MAX-MIN-VC (by Theorem 7 in [2]).

    In this section, we show Upper-PDS for bipartite graphs with maximum degree 3 is APX-complete by providing an L-reduction f from MAX-MIN-VC for bipartite graphs with maximum degree 3.

    We formalize the optimization problems as follows.

    Figure 5.   .

    Lemma 1. [11] Upper-PDS can be approximated with a factor of 2Δ for graphs without isolated vertices and with maximum degree Δ.

    Therefore, Upper-PDS is in APX.

    Let G=(V,E) be a bipartite graph with |E|=m, Δ(G)=3.

    For each edge xyE(G), let Hxy be the graph which is shown in Figure 1. Let T1={ a,...,a5, b,...,b5, r,...,r6, s,...,s6, c,..,c3, u,u1,v,v1}, T2={p,...,p6, q,...,q6, d,...,d3,w,z}, T3={h,..,h5}, T4={t,...,t5}, V(Hxy)=V(T1)V(T2)V(T3)V(T4){w1,z1,x,y}, |V(Hxy)|=70.

    Figure 1.  The graph Hxy.

    Construct G by replacing each edge xyE(G) with the graph Hxy.

    It's clear, Δ(G)=3 and G is a bipartite graph.

    Let Sp={p,p1,...,p6}, Sq={q,q1,...,q6}, Sa={a,a1,...,a5}, Sb={b,b1,...,b5}, Sr={r,r1,...,r6}, Ss={s,s1,...,s6}, Sc={c,c1,c2,c3}, Sd={d,d1,d2,d3}.

    Let xy=eE(G), He=Hxy=Hxy{x,y}, |V(Hxy)|=68.

    Let PD be a paired dominating set of G, uvE(G). We say Huv is [I,O] if u is HIuv and v is HOuv, or if v is HIuv and u is HOuv. We say Huv is [I,0] if u is HIuv and vPD, or if uPD and v is HIuv. Analogously, Huv could be [0,0] ([I,I] or [O,O] or [O,0]).

    Note that

    |T1PD|=|SaPD|+|SbPD|+|ScPD|+|SrPD|+|SsPD|+|{u,u1,v,v1}PD|, (2.1)
    |T2PD|=|SpPD|+|SqPD|+|SdPD|+|{w,z}PD|, (2.2)
    |V(Hxy)PD|=|T1PD|+|T2PD|+|T3PD|+|T4PD|+|{w1,z1}PD|. (2.3)

    The following lemma is immediate.

    Lemma 2. Let PD be a minimal paired dominating set of G, M be a perfect matching of G[PD]. If v,u are support vertices, uvE(G), xL(v), yL(u), then |{x,y}PD|1.

    Lemma 3. Let PD be a minimal paired dominating set of G, M be a perfect matching of G[PD]. For each Hxy, we have

    (a) |ScPD|=4 if and only if r,sPD, Pr(c3) or Pr(c).

    (b) |SdPD|=4 if and only if p,qPD, Pr(d3) or Pr(d).

    (c) |T3PD|4 with equality if and only if (i) or (ii) holds,

    (i) Pr(h1) if hPD,

    (ii) N(h)PD={h1} or Pr(h) if hPD.

    And if h is G[T3]O, |T3PD|=3.

    (d) |T4PD|4 with equality if and only if (i) or (ii) holds,

    (i) Pr(t1) if tPD,

    (ii) N(t)PD={t1} or Pr(t) if tPD.

    And if t is G[T4]O, |T4PD|=3.

    (e) |SaPD|4 with equality if and only if (i) or (ii) holds,

    (i) Pr(a1) if aPD,

    (ii) N(a)PD={a1} or Pr(a) if aPD.

    And if a is G[Sa]O, |SaPD|=3.

    (f) |SbPD|4 with equality if and only if (i) or (ii) holds,

    (i) Pr(b1) if bPD,

    (ii) N(b)PD={b1} or Pr(b) if bPD.

    And if b is G[Sb]O, |SbPD|=3.

    (g) 3|SrPD|4. And if rPD and r is G[Sr]O, |SrPD|=3.

    (h) 3|SsPD|4. And if sPD and s is G[Sr]O, |SsPD|=3.

    (i) 3|SpPD|4. And if pPD and p is G[Sp]O, |SpPD|=3.

    (j) 3|SqPD|4. And if qPD and q is G[Sq]O, |SqPD|=3.

    (k) |T2PD|13 with equality if and only if |{w,z}PD|=1.

    (l) |T1PD|22 with equality if and only if {u,v}PD.

    (m) If {u,v}PD=, |T1PD|18.

    Proof. (a) W.l.o.g. we consider rPD. If rcM, |ScPD|4. Otherwise, c1c2,c3sM and let PD=PD{c1,c2}, M=M{c1c2}. Then, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction. If rcM, |ScPD|4. Otherwise, cc1,c2c3M and let PD=PD{c,c1}, M=M{cc1}. Then, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction.

    If Pr(c3)= and Pr(c)=, let PD=PD{c,c3} and M=M{cc1,c2c3}{c1c2}. Then, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction.

    (b) The proof is analogous to that of (a), and the proof is omitted.

    (c) Clearly, |T3PD|4. If |T3PD|=4, {h1,h2,h3,h4}PD or {h,h1,h2,h3}PD. If {h1,h2,h3,h4}PD, Pr(h1). Otherwise, let PD=PD{h4,h1}, M=M{h3h4,h1h2}{h2h3}. Then, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction. If {h,h1,h2,h3}PD, N(h)PD={h1} or Pr(h). Otherwise, let PD=PD{h,h1}, M=M{hh1}. Then, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction.

    If h is G[T3]O, and since |T3{h}PD| is even, we have |T3PD|=3.

    (d)–(f) We obtain the conclusions with a similar proof of (c).

    (g) Clearly, 3|SrPD|6. If |SrPD|=5, we obtain SrPD={r,r1,r2,r3,r4} or SrPD={r,r2,r3,r4,r5} by Lemma 2. Therefore, PD is not a minimal paired dominating set, a contradiction. Thus, 3|SrPD|4.

    If r is G[Sr]O, and since |Sr{r}PD| is even, we have |SrPD|=3.

    (k) Since |SdPD|4, |T2PD|14 by (i)–(j) and Eq (2.1).

    If |{w,z}PD|=0, |T2PD|12.

    If |{w,z}PD|=1, |T2PD|13.

    Then we consider |{w,z}PD|=2. If w is G[T2]I or z is G[T2]I, we may assume w is G[T2]I. We obtain wpM, |SpPD|=3 by (i), |SdPD|3 by (b). Therefore, |T2PD|12 by Eq (2.1). If w,z are G[T2]O, |SdPD|3 by (b). Since |T2PD| is even, |T2PD|12 by Eq (2.1).

    Thus, |T2PD|13 with equality if and only if |{w,z}PD|=1, see Figure 2 (a).

    Figure 2.  (a) |T2PD|=13, (b) |T1PD|=22, (c) |T1PD|=18.

    (h)–(j) Using similar arguments of (g), the conclusions follow.

    (l)–(m) We discuss the following cases.

    Case 1. |{u,v}PD|=2.

    In this case, we have |{u1,v1}PD|1, |SaPD|3 and |ScPD|3, otherwise, |T1PD|22 by (e)–(h) and Eq (2.2).

    W.l.o.g. we assume u1PD.

    First, we assume that |SaPD|=4. We obtain aa1,uu1M, {r,c,r1}PD= by (e). Thus, |ScPD|3. Then, we consider |ScPD|=3, that is, c3sM. By (h), we have |SsPD|=3. Therefore, |T1PD|22 by Eq (2.2).

    Then, we consider |SaPD|=3. Therefore, v1PD, otherwise, |T1PD|22 by Eq (2.2).

    We have |SbPD|=4, otherwise, |T1PD|22 by Eq (2.2). By (f), {s,s1,c3}PD=. Thus, |ScPD|3. Therefore, |T1PD|22 by Eq (2.2), see Figure 2 (b).

    Case 2. |{u,v}PD|=1.

    W.l.o.g. we assume uPD.

    We have |{u1,v1}PD|1 and |SaPD|3, otherwise, |T1PD|21 by Eq (2.2).

    Case 2.1 u1PD.

    If |SaPD|=4, {r,r1,c}PD= by (e). Then |ScPD|3. If |ScPD|=3, we have c3sM, |SsPD|3 by (h). Therefore, |T1PD|21 by Eq (2.2). If |ScPD|=2, |T1PD|21 by Eq (2.2).

    Now we consider |SaPD|=3. If v1PD, |T1PD|21 by Eq (2.2). Thus, v1PD, that is, v1bM. Therefore, |SbPD|=3 by (f), |T1PD|21 by Eq (2.2).

    Case 2.2 u1PD.

    If v1PD, v1bM. Therefore, |SbPD|=3 by (f), |T1PD|21 by Eq (2.2). Thus, v1PD, and |T1PD|21 by Eq (2.2).

    Case 3. |{u,v}PD|=0.

    In this case, |T1PD| is even.

    Case 3.1 |{u1,v1}PD|1.

    W.l.o.g. we assume u1PD. Then u1aM, |SaPD|=3 by (e). If v1PD, bPD. By (a), |ScPD|3. Therefore, |T1PD|19 by Eq (2.2). If v1PD, we obtain v1bM, |SbPD|=3 by (f). |ScPD|3 by (a). Therefore, |T1PD|19 by Eq (2.2).

    Case 3.2 |{u1,v1}PD|=0.

    In this case, a,bPD. By (a), |ScPD|3. Therefore, |T1PD|19 by Eq (2.2).

    Note that |T1PD| is even, so |T1PD|18, see Figure 2 (c).

    Thus, (l) and (m) hold.

    Lemma 4. Let PD be a minimal paired dominating set of G.

    (a) |V(Hxy)PD|43.

    (b) If {xw1,yz1}M, |V(Hxy)PD|42.

    (c) If {xw1,yz1}M= and {w1,z1}PD, |V(Hxy)PD|42.

    (d) If xw1M(G), w1PD and yPD, then |V(Hxy)PD|42.

    Proof. (a) By Lemma 3 and Eq (2.3),

    |V(Hxy)PD|=|T1PD|+|T2PD|+|T3PD|+|T4PD|+|{w1,z1}PD|22+13+4+4+2=45.

    We consider that {w1,z1}PD, |T4PD|3 and |T3PD|3, otherwise, |V(Hxy)PD|43. Then, w.l.o.g. we assume that w1PD.

    If |T4PD|=4, {tt1,t2t3}M or {t1t2,t3t4}M.

    If {tt1,t2t3}M, Pr(t) or N(t)PD={t1}. If Pr(t), uPr(t). By Lemma 3 (m), |V(Hxy)PD|43. If N(t)PD={t1}, we have u,w1PD, |T1PD|21 by Lemma 3 (l). Then we obtain zPD, otherwise, |V(Hxy)PD|43 by Lemma 3 (k) and Eq (2.3). If |T3PD|=4, we have vPD, therefore, |V(Hxy)PD|43 by Eq (2.3). If |T3PD|=3, |V(Hxy)PD|43 by Eq (2.3).

    If {t1t2,t3t4}M, {w,u}PD=. By Lemma 3 (l), |T1PD|21, and vPD. If zPD, |V(Hxy)PD|43 by Lemma 3 (k) and Eq (2.3). If zPD, |T3PD|3 by Lemma 3 (c). Therefore, |V(Hxy)PD|43 by Eq (2.3).

    If |T4PD|=3, we consider |T1PD|=22, and {u,v,z1}PD. We have |T3PD|4 by Lemma 3 (c). Therefore, |V(Hxy)PD|43 by Eq (2.3).

    (b)–(d) Since |V(Hxy)PD|43, and, |V(Hxy)PD| is even in those cases, so |V(Hxy)PD|42.

    Lemma 5. Let PD be a minimal paired dominating set of G, M be a perfect matching of G[PD].

    (a) If {x,w1,y,z1}PD, xw1M(G) and yz1M, we have |V(Hxy)PD|41.

    (b) If {x,y}PD=, |V(Hxy)PD|40.

    Proof. (a) In this case, we have zPD and zz1M.

    Since |V(Hxy)PD| is odd, it's sufficient to show |V(Hxy)PD|42. We only consider {u,v}PD by Lemma 3 (m).

    Case 1. |T4PD|=4.

    In this case, we have {t1t2,t3t4}M or {tt1,t2t3}M. If {t1t2,t3t4}M (or {tt1,t2t3}M), we obtain u,wPD, vPD. Since z,vPD, |T3PD|3 by Lemma 3 (c). If |T3PD|=2, |V(Hxy)PD|42 by Eq (2.3). If |T3PD|=3, hvM. Thus, {q,q1,d3}PD=, otherwise, let PD=PD{z,z1}, M=M{zz1}. Then, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction. Since zz1M, we obtain that |T2PD| is odd. So |T1PD|12. Therefore, |V(Hxy)PD|42 by Eq (2.3), see Figure 3 (a).

    Figure 3.  (a) |V(Hxy)PD|=41, (b) |V(Hxy)PD|=40.

    Case 2. |T4PD|=3.

    If twM, uPD or {p,p1,d}PD=. Otherwise, let PD=PD{t,w}, M=M{tw}. Then, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction. If {p,p1,d}PD=, |SdPD|3. W have |SdPD|=3, d3qM, otherwise, |V(Hxy)PD|42 by Eq (2.3). Thus |SqPD|3 by Lemma 3 (j) and Eq (2.3), and |V(Hxy)PD|42. If uPD, vPD. Thus, |T3PD|3 by Lemma 3 (c) and Eq (2.3), and |V(Hxy)PD|42.

    If tuM, |T3PD|3 by Lemma 3 (c). We have |T3PD|=3, hvM, otherwise, |V(Hxy)PD|42 by Eq (2.3). Let PD=PD{t,h}, M=M{tu,hv}{uv}. Therefore, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction.

    Case 3. |T4PD|=2.

    Now we only consider |T1PD|=22, and {u,v}PD. By Lemma 3 (c) and Eq (2.3), |V(Hxy)PD|42.

    (b) Since |V(Hxy)PD| is even, it's sufficient to show |V(Hxy)PD|41.

    Case 1. |{z1,w1}PD|=0.

    We obtain {z,w}PD, |T2PD|12 by Lemma 3 (k). If |T4PD|3, |V(Hxy)PD|41 by Eq (2.3), see Figure 3 (b). If |T4PD|=4, tPD and Pr(t) by Lemma 3(d). So, {u,v,u1}PD=. By Lemma 3 (m) and Eq (2.3), |V(Hxy)PD|40.

    Case 2. |{z1,w1}PD|=1.

    W.l.o.g. we assume w1PD. Thus, ww1M, zPD, |T2PD|12 by Lemma 3 (k). If |T4PD|=2, |V(Hxy)PD|41 by Eq (2.3). If |T4PD|=4, we obtain Pr(t)={u} for tPD, {u,u1,v}PD=. By Lemma 3 (m) and Eq (2.3), |V(Hxy)PD|40. If |T4PD|=3, tuM. And vPD, otherwise |T1PD|21 by Lemma 3 (l), |V(Hxy)PD|41 by Eq (2.3). Thus, |T4PD|4 by Lemma 3 (d). Afterwards, |V(Hxy)PD|41 by Eq (2.3).

    Case 3. |{z1,w1}PD|=2.

    Thus, ww1M, zz1M, |T2PD|12 by Lemma 3 (k).

    If |T4PD|=4, tPD and {u,u1,v}PD=. By Lemma 3 (m) and Eq (2.3), we have |V(Hxy)PD|40.

    If |T4PD|=3, we have tuM, and |T3PD|3 by Lemma 3 (c). If |T3PD|=2, |V(Hxy)PD|41 by Eq (2.3). If |T3PD|=3, hvM. Let PD=PD{t,h}, M=M{tu,hv}{uv}. Therefore, PD is a paired dominating set and PD is not a minimal paired dominating set, a contradiction.

    If |T4PD|=2, we only consider |T1PD|=22. Thus, u,vPD. By Lemma 3 (c), |T3PD|3. Therefore, |V(Hxy)PD|41 by Eq (2.3).

    Corollary 6. Let PD be a minimal paired dominating set of G. If |V(Huv)PD|=43 if and only if |{u,v}PD|=1, and, u or v is HIuv.

    Lemma 7. If VC1 is a minimal vertex cover of G, there exists a minimal paired dominating set PD1 of G with |PD1|=42m+2|VC|.

    Proof. A minimal paired dominating set PD1 can be constructed by the following manner:

    For each vertex xVC1, we have |N(x)VC1|<d(x)3. So there exists at least one edge xx1 with x1VC1 in G, and maybe exist edges xx2 or xx3.

    Therefore, for the edge xx1, put i into PD for i{x,w1, p2,p3,p4,p5, d,d1,d2,d3, q2,q3,q4,q5, z,z1, h,h2,h3, v, b1,b2,b3,b4, s2,s3,s4,s5, c,c1,c2,c3, r2,r3,r4,r5, a,a1,a2,a3, t1,t2,t3,t4}. Put j into M for j{xw1, p5p4,p3p2,dd1,d2d3, q2q3,q4q5, zz1, hv, h2h3, b1b2,b3b4, s2s3,s4s5, cc1,c2c3, r2r3,r4r5, aa1,a2a3, t1t2,t3t4}. See Figure 4 (a).

    Figure 4.  (a) |V(Hxy)PD|=43, (b) |V(Hxy)PD|=42.

    For edges xx2,xx3, put i into PD for i{x, p2,p3,p4,p5, d,d1,d2,d3, q2,q3,q4,q5, z,z1, u,v, h2,h3, b1,b2,b3,b4, s2,s3,s4,s5, c,c1,c2,c3, r2,r3,r4,r5, a1,a2,a3,a4, t1,t2,t3,t4}. Put j into M for j{ p5p4,p3p2,dd1,d2d3, q2q3,q4q5, zz1, h2h3, uv, b1b2,b3b4, s2s3,s4s5, cc1,c2c3, r2r3,r4r5, a1a2,a3a4, tt1,t2t3}. See Figure 4 (b).

    Let PD1=PDVC1. Since vertex x is M-saturated in PD1. Therefore, PD1 is a paired dominating set of G.

    Since N(w)PD1={w1}, then PD1{w1} is not a dominating set of G. So PD1 is a minimal paired dominating set of G. And |PD1|=|VC1|+|VC1|×43+(m|VC1|)×42. Therefore, |PD1|=2|VC1|+42m.

    Let PD be a minimal paired dominating set of G. Algorithm 1 is to obtain a minimal vertex cover VC of G, and it terminates in polynomial time.

    Algorithm 1 CONST-VC(G,PD)
    Input: A graph G with a minimal paired dominating set PD
    Output: A graph G with a minimal vertex cover VC
    1: VC=PD
    2: for every HxyG do
    3:  Delete vertices in Hxy
    4:  Add an edge between x and y {obtained the graph G}
    5: VC=VCV(Hxy)
    6: end for
    7: VC=VC
    8: De= {Mo is the set of vertex which is removed from VC.}
    9: In= {In is the set of vertex which is added into VC.}
    10: Mo= {De is the set of vertex which is added into VC at first, then removed from VC.}
    11: while |N[v]VC|=d(v)+1 do
    12:  VC=VC{v},Mo=Mo{v}
    13: end while
    14: while uvE(G) and u,vVC do
    15:  VC=VC{u},In=In{u}
    16:  for wN(u) do
    17:   if |N[w]VC|=d(w)+1 then
    18:    VC=VC{w},De=De{w}
    19:   end if
    20:  end for
    21: end while
    22: return VC

     | Show Table
    DownLoad: CSV

    Lemma 8. If PD is a minimal paired dominating set of G and VC is a minimal vertex cover of G obtained by Algorithm 1, |VC||PD|42m|VC|.

    Proof. Let M be the perfect matching of G[PD], me=V(Hxy)PD where e=xyE(G), Me=eE(G)me, Le=V(G)(MoInDe).

    In Algorithm 1, we have:

    Claim 9. (a) If v is put into Mo by the while loop (lines 11 to 13) or De (line 18), v will not be put into In later.

    (b) For every vertex vV(G), v will be put into Mo (or De or In) at most once.

    (c) MoDe=, MoIn=.

    (d) If vDe, there exists a vertex wN(v)In.

    (e) If vertex vDeIn, we have vVC, that is, v is put into In at first and then into De.

    (f) If u,vDeMo, N(v)N(u)MoDe=.

    (g) If vDeIn, there exists a vertex uN(v)(InDe), uVC. And |N(u)De|2. What's more, there exists a vertex wN(u)VC. If wInDe, |(N(u)N(w))(DeIn)|3.

    Proof. (a) After v is put into De (or Mo), every wN(v) has a neighbor v which does not belong to VC, so w will not be put into De. Therefore, v will not be put into In later.

    (b)–(d) By (a), it is immediate.

    (e) By (a) and (c), it is immediate.

    (f) Suppose v is put into DeMo. By (a), wN(v) will not be put into DeMo.

    (g) For vertex vDeIn, by (d) and (f), let uN(v)(InDe), and uVC, |N(u)De|2.

    Since uInDe, there exists a vertex wN(u)VC.

    Since 1|N(u)(DeIn)|2, |N(w)(DeIn)|2. If wInDe, we may assume u is put into In at first. Then N(u)(DeIn)|1, otherwise, w will not be put into In later. Therefore, |(N(u)N(w))(DeIn)|3.

    Thus,

    |VC|=|PD||Me||Mo||De|+|In|. (2.4)

    To show that |Me|+|Mo|+|De||In|42m+|VC|, we use the following strategy.

    Discharging procedure:

    In the graph G, we set the initial charge of every vertex v to be s(v)=1 for vMoMe(DeIn), s(v)=1 for vInDe, s(v)=0 otherwise, s(Huv)=xV(Huv)s(x), s(G)=vV(G)s(v).

    Obviously,

    vV(G)s(v)=|Me|+|Mo|+|De||In|. (2.5)

    We use the discharging procedure, leading to a final charge s, defined by applying the following rules:

    Rule 1: For the vertex vMo, v is M-saturated. Therefore, v is HIuv for u. If u is HIuv, s(v) transmits 1 charge to s(u). If u is HOuv, s(v) transmits 1 charge to s(Huv) which is [I,O].

    Rule 2: For each s(Huv)=43, by Corollary 6, s(Huv) transmits 1 charge to uVC.

    Rule 3: For the vertex vDeIn, by Claim 9 (g), there exists a vertex uN(v)(InDe), and a vertex wN(u)VC and |N(u)De|2. If |N(u)De|=2, s(v) transmits 1 charge to s(u) and transmits 1 charge to s(Huw) which is [0,0]. If |N(u)De|=1, s(v) transmits 2 charge to s(u).

    After discharging, we have:

    Claim 10. (a) s(v)0 for vMo(DeIn)(LeVC)(InDe).

    (b) For each Hxy, s(Hxy)42.

    (c) s(v)1 for v(InDe)(LeVC).

    Proof. (a) If vMo, by Claim 9 (f), v will not receive any charge by Rules 1 and 3. Since N[v]VC=N[v]. By Lemmas 4 and 5, v will not receive any charge by Rule 2. Therefore, s(v)=0.

    If vDeIn, vVC. By Claim 9 (f), N(v)Mo=. Thus, v will not receive any charge by Rules 1 and 3. Since v is HIuv for u. By Lemmas 4 and 5, if uVC, v will not receive any charge by Rule 2. If uVC, v will receive 1 charge at most by Rule 2. Afterwards, by Rule 3, v will transmit 2 charge to others, so s(v)0.

    If vLeVC, v will not receive any charge by Rules 1, 2 and 3.

    If vInDe, vVC by Claim 9 (e). Thus, v will not receive any charge by Rules 1 and 2. By Claim 9 (f), vDe, N(v)De=. Thus, v will not receive any charge by Rule 3.

    (b) If Huw is [I,I] or [O,O] or [I,0] or [O,0], s(Huw) will not receive any charge by Rules 1, 2 and 3. If Huw is [0,0], s(Huw) will not receive any charge by Rules 1 and 2.

    If Huw is [0,0], by Claim 9 (g), |(N(u)N(w))(DeIn)|3. Thus, s(Huw) will receive 2 charge at most from s(x) where xN(v){w} by Rule 3.

    And if s(Huw)=43, by Corollary 6, there exists a vertex uVC and u is HIuw. Therefore, s(Huw)=42 by Rule 2.

    Thus, by Lemmas 4 and 5, s(Huw)42.

    (c) If vInDe, vVC, v will receive any charge by Rules 1 and 2. And there exists a vertex wN(v) wVC and wDeIn. So v will receive 2 charge at most by Rule 3, s(v)1+2=1.

    If vLeVC, v will receive any charge by Rule 3. By Lemmas 4, 5 and Corollary 6, Huv is [I,0] if s(Huv)=43. Since v can be M-saturated once, v will receive 1 charge at most by Rules 1 and 2. Thus, s(v)0+1=1.

    By Claim 10,

    |Me|+|Mo|+|De||In|=uvE(G)s(Huv)+vMos(v)+vDeIns(v)vInDes(v)=uvE(G)s(Huv)+vMos(v)+vDeIns(v)+vInDes(v)+vInDes(v)+vLeVCs(v)vLeVCs(v)42m+|InDe|+|LeVC|42m+|VC|.

    Thus, by Eq (2.4),

    |VC|=|PD||Me||Mo||De|+|In||PD|42m|VC|.

    Let PD be a Γpr(G)-set of G, and be the Input of Algorithm 1. Then we obtain the Output VC by Algorithm 1.

    Since

    |VC|mΔ=m3.

    By Lemma 8,

    |VC||PD|42m|VC||PD|42×3|VC||VC||VC||PD|127|VC|

    Let VC be a VC-set of G. Since |VC||VC|,

    |PD|128|VC|128|VC| (2.6)

    By Lemma 7, |PD||PD1|=42m+2|VC|. By Lemma 8,

    |PD||VC||VC|+42m|VC|+42m|PD||VC|.

    Thus,

    |VC||VC||PD||PD| (2.7)

    Therefore, by Eq (2.6) and Eq (2.7), f is an L-reduction with α=128, β=1.

    Upper-PDS for bipartite graphs is proved to be APX-complete with maximum degree 4 and still open with maximum degree 3. In this paper, we show that Upper-PDS for bipartite graphs with maximum degree 3 is APX-complete by providing an L-reduction f from MAX-MIN-VC for bipartite graphs to it.

    This work was supported by the National Key R & D Program of China (No. 2018YFB1005100), the Guangzhou Academician and Expert Workstation (No. 20200115-9) and the Innovation Ability Training Program for Doctoral student of Guangzhou University (No. 2019GDJC-D01).

    The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.



    [1] H. Fan, Financial analysis in CATL based on Harvard analysis framework, 2022 2nd International Conference on Enterprise Management and Economic Development (ICEMED 2022), 2022,874–879. https://doi.org/10.2991/aebmr.k.220603.143
    [2] H. Liu, H. Liu, H. Wang, Research of power battery risk investment: taking CATL as an example, Financ. Eng. Risk Manag., 6 (2023), 5. https://doi.org/10.23977/ferm.2023.060501 doi: 10.23977/ferm.2023.060501
    [3] D. Lyu, Analyzing the risk-return trade-off relationship of Chinese new energy firms using the capital asset pricing model, High. Bus. Econ. Manag., 30 (2024), 429–435. https://doi.org/10.54097/fwj7mr52 doi: 10.23977/ferm.2023.060501
    [4] K. Hayashi, K. Nakagawa, Fractional SDE-net: generation of time series data with long-term memory, 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 2022. https://doi.org/10.1109/DSAA54385.2022.10032351
    [5] P. Kidger, J. Foster, X. Li, T. J. Lyons, Neural SDEs as infinite-dimensional GANs, International Conference on Machine Learning, 2021, 5453–5463. Available form: https://proceedings.mlr.press/v139/kidger21b.html.
    [6] F. A. Longstaff, E. S. Schwartz, Valuing American options by simulation: a simple least-squares approach, Rev. Financ. Stud., 14 (2001), 113–147. https://doi.org/10.1093/rfs/14.1.113 doi: 10.1093/rfs/14.1.113
    [7] P. A. Sadorsky, A random forests approach to predicting clean energy stock prices, J. Risk. Financ. Manag., 14 (2021), 48. https://doi.org/10.3390/jrfm14020048 doi: 10.3390/jrfm14020048
    [8] P. A. Sadorsky, Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices? North Amer. J. Econ. Finance, 61 (2022), 101705. https://doi.org/10.1016/j.najef.2022.101705 doi: 10.1016/j.najef.2022.101705
    [9] M. Wang, Z. Xiao, H. Peng, X. Wang, J. Wang, Stock price prediction for new energy vehicle enterprises: an integrated method based on time series and cloud models, Expert. Syst. Appl., 208 (2022), 118125. https://doi.org/10.1016/j.eswa.2022.118125 doi: 10.1016/j.eswa.2022.118125
    [10] L. Gu, C. Feng, Research on stock price prediction based on Markov-LSTM neural network-take the new energy industry as an example, Acad. J. Bus. Manag., 4 (2022), 42–47. https://doi.org/10.25236/AJBM.2022.040408 doi: 10.25236/AJBM.2022.040408
    [11] A. Meng, P. Wang, G. Zhai, C. Zeng, S. Chen, X. Yang, et al., Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization, Energy, 254 (2022), 124212. https://doi.org/10.1016/j.energy.2022.124212 doi: 10.1016/j.energy.2022.124212
    [12] Q. Shen, Y. Zhang, J. Xiao, X. Dong, Z. Lin, Research of daily stock closing price prediction for new energy companies in China, Data. Sci. Finance Econ., 3 (2023), 14–29. https://doi.org/10.3934/DSFE.2023002 doi: 10.3934/DSFE.2023002
    [13] Q. Zhu, X. Zhou, S. Liu, High return and low risk: shaping composite financial investment decision in the new energy stock market, Energy Econ., 122, (2023), 106683. https://doi.org/10.1016/j.eneco.2023.106683 doi: 10.1016/j.eneco.2023.106683
    [14] G. F. Fan, R. T. Zhang, C. C. Cao, L. L. Peng, Y. H. Yeh, W. C. Hong, The volatility mechanism and intelligent fusion forecast of new energy stock prices, Financ. Innov., 10 (2024), 84. https://doi.org/10.1186/s40854-024-00621-7 doi: 10.1186/s40854-024-00621-7
    [15] M. M. Alshater, I. Kampouris, H. Marashdeh, O. F. Atayah, H. Banna, Early warning system to predict energy prices: the role of artificial intelligence and machine learning, Ann. Oper. Res., 2022. https://doi.org/10.1007/s10479-022-04908-9
    [16] H. Gao, G. Kou, H. Liang, H. Zhang, X. Chao, C. Li, Y. Dong, Machine learning in business and finance: a literature review and research opportunities, Financ. Innov., 10 (2024), 86. https://doi.org/10.1186/s40854-024-00629-z doi: 10.1186/s40854-024-00629-z
    [17] I. Ghosh, R. K. Jana, Clean energy stock price forecasting and response to macroeconomic variables: a novel framework using Facebook's prophet, neuralprophet and explainable AI, Technol. Forecast. Soc. Change, 200 (2024), 123148. https://doi.org/10.1016/j.techfore.2023.123148 doi: 10.1016/j.techfore.2023.123148
    [18] L. Jiang, G. Hu, A review on short-term electricity price forecasting techniques for energy markets, 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2018,937–944. https://doi.org/10.1109/ICARCV.2018.8581312
    [19] Y. Lin, Q. Lu, B. Tan, Y. Yu, Forecasting energy prices using a novel hybrid model with variational mode decomposition, Energy, 246 (2023), 123366. https://doi.org/10.1016/j.energy.2022.123366 doi: 10.1016/j.energy.2022.123366
    [20] M. M. Mostafa, A. A. El-Masry, Oil price forecasting using gene expression programming and artificial neural networks, Econ. Model., 54 (2016), 40–53. https://doi.org/10.1016/j.econmod.2015.12.014 doi: 10.1016/j.econmod.2015.12.014
    [21] R. Pino, J. Parreno, A. Gomez, P. Priore, Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks, Eng. Appl. Artif. Intell., 21 (2008), 53–62. https://doi.org/10.1016/j.engappai.2007.02.001 doi: 10.1016/j.engappai.2007.02.001
    [22] G. J. Lord, C. E. Powell, T. Shardlow, An introduction to computational stochastic PDEs, Cambridge University Press, 2014. https://doi.org/10.1017/CBO9781139017329
    [23] F. Biagini, Y. Hu, B. Øksendal, T. Zhang, Stochastic calculus for fractional Brownian motion and applications, Springer Science & Business Media Publishers, 2008. https://doi.org/10.1007/978-1-84628-797-8
    [24] X. An, X. Yi, B. Zhou, Q. Zhang, Research on stock market investment model based on time series forecasting and dynamic programming, Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023), 2023,412–420. https://doi.org/10.2991/978-94-6463-270-5_46
    [25] T. Wang, J. Guo, Y. Shan, Y. Zhang, B. Peng, Z. Wu, A knowledge graph-GCN-community detection integrated model for large-scale stock price prediction, Appl. Soft. Comput., 145 (2023), 110595. https://doi.org/10.1016/j.asoc.2023.110595 doi: 10.1016/j.asoc.2023.110595
    [26] B. Mandelbrot, Statistical methodology for nonperiodic cycles: from the covariance to R/S analysis, Nber, 1972,259–290.
    [27] M. J. Wainwright, M. I. Jordan, Graphical models, exponential families, and variational inference, Found. Trends Mach. Learn., 1 (2008), 1–305. https://doi.org/10.1561/2200000001 doi: 10.1561/2200000001
    [28] H. Zhang, I. Goodfellow, D. Metaxas, A. Odena, Self-attention generative adversarial networks, Proceedings of the 36th International Conference on Machine Learning, 2019.
    [29] P. Kidger, J. Morrill, J. Foster, T. Lyons, Neural controlled differential equations for irregular time series, Adv. Neural Inf. Process. Syst., 33 (2020), 6696–6707.
    [30] M. Arjovsky, S. Chintala, L. Bottou, Wasserstein generative adversarial networks, ICML'17: Proceedings of the 34th International Conference on Machine Learning, 70 (2017), 214–223.
    [31] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Generative adversarial nets, Adv. Neural Inf. Process. Syst., 2 (2021), 2672–2680.
    [32] X. Li, T. K. L. Wong, R. T. Chen, D. K. Duvenaud, Scalable gradients and variational inference for stochastic differential equations, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, 2020.
    [33] A. Q. Md, S. Kapoor, A. V. C. Junni, A. K. Sivaraman, K. F. Tee, H. Sabireen, et al., Novel optimization approach for stock price forecasting using multi-layered sequential LSTM, Appl. Soft Comput., 134 (2023), 109830. https://doi.org/10.1016/j.asoc.2022.109830 doi: 10.1016/j.asoc.2022.109830
    [34] U. S. M. de Lima, C. P. Samanez, Complex derivatives valuation: applying the least-squares Monte Carlo simulation method with several polynomial basis, Financ. Innov., 2 (2018), 1. https://doi.org/10.1186/s40854-015-0019-0 doi: 10.1186/s40854-015-0019-0
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(729) PDF downloads(51) Cited by(0)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog