# Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Description of the Model

#### 2.1. On a Single Layer RVFL Network

- The inclusion of a linear dependence between the output variable and the predictors: the direct link, ${\beta}_{0}+{\sum}_{j=1}^{p}{\beta}_{j}{Z}_{i}^{(j)}$.
- The elements ${W}^{(j,l)}$ of the hidden layer are typically not trained, but randomly and uniformly chosen on a given interval. Different ranges for these elements of the hidden layer are tested in (Zhang and Suganthan 2016).

- The bitwise exclusive-or operation ⊕ applied to two integers p and $q\in \left\{0,1\right\}$ returns 1 if and only if one of the two (but not both) inputs is equal to 1. Otherwise, $p\oplus q$ is equal to 0.
- The second term of the equation relies on primitive polynomials of degree ${s}_{j}$, with coefficients ${a}_{i,j}$ taken in $\left\{0,1\right\}$:$${x}^{{s}_{j}}+{a}_{1,j}{x}^{{s}_{j}-1}+{a}_{2,j}{x}^{{s}_{j}-2}+\dots +{a}_{{s}_{j}-1,j}x+1$$
- The terms ${m}_{k,j}$ are obtained recursively, with the initial values ${m}_{1,j},{m}_{2,j},\dots ,{m}_{k-{s}_{j},j}$ chosen freely, under the condition that ${m}_{k,j},1\le k\le {s}_{j}$ is odd and less than ${2}^{k}$.

#### 2.2. Applying RVFL Networks to Multivariate Time Series Forecasting

#### 2.3. Solving for $\widehat{\beta}$’s and $\widehat{\gamma}$’s

#### 2.4. h-Steps Ahead Forecasts and Use of Dynamic Regression

## 3. Numerical Examples

#### 3.1. A Dynamic Nelson-Siegel Example

#### 3.2. Forecasting 1 Year, 10 Years and 20 Years Spot Rates

#### 3.3. Forecasting on a Longer Horizon, with a Longer Training Window

`auto.arima`and the RVFL model presented in this paper, applied to the three factors. In the fashion of Section 3.1. However, now we obtain 36-month’s ahead forecasts, from a rolling training windows with a fixed 36 month’s length. The average out-of-sample $RMSE$ are then calculated for each method.

- DNS with ARIMA (
`auto.arima`): $\lambda =1.4271$ (Nelson Siegel parameter). - DNS with RVFL: number of lags for each series: 1, activation function: ReLU, number of nodes in the hidden layer: 45, ${\lambda}_{1}=4.6416$, ${\lambda}_{2}=774.2637$ (RVFL parameters) and $\lambda =24$ (Nelson Siegel parameter).

## 4. Conclusions

## Author Contributions

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Mean Forecast and Confidence Intervals for α_{i,t}, i = 1,…,3 Forecasts

`alpha1 y_lo80 y_hi80 y_lo95 y_hi95`

`13 0.7432724 0.6852024 0.8013425 0.6544620 0.8320829`

`14 0.7357374 0.6776673 0.7938074 0.6469269 0.8245478`

`15 0.7378042 0.6797342 0.7958742 0.6489938 0.8266147`

`16 0.7408417 0.6827717 0.7989118 0.6520313 0.8296522`

`17 0.7407904 0.6827204 0.7988604 0.6519800 0.8296009`

`18 0.7404501 0.6823801 0.7985201 0.6516396 0.8292605`

`19 0.7403603 0.6822903 0.7984303 0.6515498 0.8291707`

`20 0.7403981 0.6823281 0.7984681 0.6515876 0.8292085`

`21 0.7404788 0.6824087 0.7985488 0.6516683 0.8292892`

`22 0.7404786 0.6824086 0.7985487 0.6516682 0.8292891`

`23 0.7404791 0.6824091 0.7985491 0.6516686 0.8292895`

`24 0.7404758 0.6824058 0.7985458 0.6516654 0.8292863`

`alpha2 y_lo80 y_hi80 y_lo95 y_hi95`

`13 -1.250640 -1.351785 -1.149495 -1.405328 -1.095952`

`14 -1.243294 -1.344439 -1.142149 -1.397982 -1.088606`

`15 -1.241429 -1.342574 -1.140284 -1.396117 -1.086741`

`16 -1.243868 -1.345014 -1.142723 -1.398557 -1.089180`

`17 -1.244483 -1.345628 -1.143338 -1.399171 -1.089795`

`18 -1.241865 -1.343010 -1.140719 -1.396553 -1.087177`

`19 -1.240814 -1.341959 -1.139669 -1.395502 -1.086126`

`20 -1.240371 -1.341516 -1.139226 -1.395059 -1.085683`

`21 -1.240237 -1.341382 -1.139092 -1.394925 -1.085549`

`22 -1.240276 -1.341421 -1.139131 -1.394964 -1.085588`

`23 -1.240329 -1.341474 -1.139184 -1.395017 -1.085641`

`24 -1.240308 -1.341453 -1.139163 -1.394996 -1.085620`

`alpha3 y_lo80 y_hi80 y_lo95 y_hi95`

`13 4.584836 4.328843 4.757406 4.215410 4.870840`

`14 4.546167 4.307253 4.849862 4.163634 4.993482`

`15 4.527651 4.201991 4.803004 4.042913 4.962082`

`16 4.513810 4.216525 4.850160 4.048812 5.017874`

`17 4.517643 4.176214 4.828735 4.003502 5.001446`

`18 4.523109 4.203064 4.866713 4.027406 5.042371`

`19 4.522772 4.178489 4.848760 4.001079 5.026170`

`20 4.521846 4.191833 4.866066 4.013374 5.044524`

`21 4.521382 4.177560 4.854171 3.998472 5.033259`

`22 4.521451 4.186714 4.864755 4.007248 5.044222`

`23 4.521772 4.178995 4.857897 3.999300 5.037591`

`24 4.521862 4.184734 4.864155 4.004903 5.043987`

#### Appendix A.2. Stressed Forecast (α_{1,t} + 0.5%) and Confidence Intervals for α_{i,t}, i = 1,…,3 Forecasts

`alpha1 y_lo80 y_hi80 y_lo95 y_hi95`

`13 1.25 1.19193 1.30807 1.16119 1.33881`

`14 1.25 1.19193 1.30807 1.16119 1.33881`

`15 1.25 1.19193 1.30807 1.16119 1.33881`

`16 1.25 1.19193 1.30807 1.16119 1.33881`

`17 1.25 1.19193 1.30807 1.16119 1.33881`

`18 1.25 1.19193 1.30807 1.16119 1.33881`

`19 1.25 1.19193 1.30807 1.16119 1.33881`

`20 1.25 1.19193 1.30807 1.16119 1.33881`

`21 1.25 1.19193 1.30807 1.16119 1.33881`

`22 1.25 1.19193 1.30807 1.16119 1.33881`

`23 1.25 1.19193 1.30807 1.16119 1.33881`

`24 1.25 1.19193 1.30807 1.16119 1.33881`

`alpha2 y_lo80 y_hi80 y_lo95 y_hi95`

`13 -1.222568 -1.323713 -1.121423 -1.377256 -1.067880`

`14 -1.219401 -1.320546 -1.118256 -1.374089 -1.064713`

`15 -1.211361 -1.312506 -1.110216 -1.366049 -1.056673`

`16 -1.216213 -1.317358 -1.115068 -1.370901 -1.061525`

`17 -1.215266 -1.316411 -1.114121 -1.369954 -1.060578`

`18 -1.211474 -1.312619 -1.110329 -1.366162 -1.056786`

`19 -1.210329 -1.311474 -1.109183 -1.365017 -1.055640`

`20 -1.209610 -1.310755 -1.108465 -1.364298 -1.054922`

`21 -1.209648 -1.310793 -1.108503 -1.364336 -1.054960`

`22 -1.209601 -1.310746 -1.108456 -1.364289 -1.054913`

`23 -1.209688 -1.310833 -1.108542 -1.364376 -1.055000`

`24 -1.209653 -1.310798 -1.108508 -1.364341 -1.054965`

`alpha3 y_lo80 y_hi80 y_lo95 y_hi95`

`13 4.500390 4.244398 4.672961 4.130964 4.786394`

`14 4.482948 4.244035 4.786643 4.100415 4.930263`

`15 4.441841 4.116182 4.717194 3.957103 4.876272`

`16 4.441744 4.144459 4.778094 3.976746 4.945808`

`17 4.439609 4.098181 4.750701 3.925469 4.923413`

`18 4.447151 4.127105 4.790755 3.951448 4.966412`

`19 4.446164 4.101881 4.772152 3.924471 4.949562`

`20 4.445421 4.115407 4.789641 3.936949 4.968099`

`21 4.445411 4.101589 4.778200 3.922501 4.957289`

`22 4.445491 4.110754 4.788795 3.931287 4.968262`

`23 4.445937 4.103160 4.782062 3.923465 4.961756`

`24 4.446012 4.108885 4.788306 3.929053 4.968137`

#### Appendix A.3. Out-of-Sample log(RMSE) as a Function of log(λ_{1}) and log(λ_{2})

`log_lambda1 log_lambda2 log_error`

`1 -4.605170 -4.605170 13.5039336`

`2 -3.070113 -4.605170 14.3538621`

`3 -1.535057 -4.605170 14.7912525`

`4 0.000000 -4.605170 14.8749375`

`5 1.535057 -4.605170 14.8928025`

`6 3.070113 -4.605170 14.8962203`

`7 4.605170 -4.605170 14.8975776`

`8 6.140227 -4.605170 14.8976494`

`9 7.675284 -4.605170 14.8976630`

`10 9.210340 -4.605170 14.8976715`

`11 -4.605170 -3.070113 3.9701229`

`12 -3.070113 -3.070113 4.9644144`

`13 -1.535057 -3.070113 5.2607788`

`14 0.000000 -3.070113 5.3208833`

`15 1.535057 -3.070113 5.3326856`

`16 3.070113 -3.070113 5.3351784`

`17 4.605170 -3.070113 5.3357136`

`18 6.140227 -3.070113 5.3358306`

`19 7.675284 -3.070113 5.3358557`

`20 9.210340 -3.070113 5.3358610`

`21 -4.605170 -1.535057 3.6692928`

`22 -3.070113 -1.535057 3.5643607`

`23 -1.535057 -1.535057 3.5063072`

`24 0.000000 -1.535057 3.4942438`

`25 1.535057 -1.535057 3.4904354`

`26 3.070113 -1.535057 3.4881352`

`27 4.605170 -1.535057 3.4888202`

`28 6.140227 -1.535057 3.4880668`

`29 7.675284 -1.535057 3.4886911`

`30 9.210340 -1.535057 3.4886383`

`31 -4.605170 0.000000 3.6224691`

`32 -3.070113 0.000000 3.8313407`

`33 -1.535057 0.000000 3.8518759`

`34 0.000000 0.000000 3.8163287`

`35 1.535057 0.000000 3.7993471`

`36 3.070113 0.000000 3.7948073`

`37 4.605170 0.000000 3.7937787`

`38 6.140227 0.000000 3.7935546`

`39 7.675284 0.000000 3.7935063`

`40 9.210340 0.000000 3.7934958`

`41 -4.605170 1.535057 -1.2115597`

`42 -3.070113 1.535057 -1.0130537`

`43 -1.535057 1.535057 -0.5841784`

`44 0.000000 1.535057 -0.3802817`

`45 1.535057 1.535057 -0.3109827`

`46 3.070113 1.535057 -0.2931830`

`47 4.605170 1.535057 -0.2891586`

`48 6.140227 1.535057 -0.2882824`

`49 7.675284 1.535057 -0.2880932`

`50 9.210340 1.535057 -0.2880524`

`51 -4.605170 3.070113 -2.0397856`

`52 -3.070113 3.070113 -3.1729003`

`53 -1.535057 3.070113 -3.8655596`

`54 0.000000 3.070113 -3.9279840`

`55 1.535057 3.070113 -3.9508440`

`56 3.070113 3.070113 -4.0270316`

`57 4.605170 3.070113 -4.0831569`

`58 6.140227 3.070113 -4.0953167`

`59 7.675284 3.070113 -4.0979447`

`60 9.210340 3.070113 -4.0985113`

`61 -4.605170 4.605170 -2.6779260`

`62 -3.070113 4.605170 -3.4704808`

`63 -1.535057 4.605170 -4.0404935`

`64 0.000000 4.605170 -4.2441836`

`65 1.535057 4.605170 -4.3657802`

`66 3.070113 4.605170 -4.3923094`

`67 4.605170 4.605170 -4.3862826`

`68 6.140227 4.605170 -4.3830293`

`69 7.675284 4.605170 -4.3820703`

`70 9.210340 4.605170 -4.3818486`

`71 -4.605170 6.140227 -3.5007058`

`72 -3.070113 6.140227 -3.8389274`

`73 -1.535057 6.140227 -4.1969545`

`74 0.000000 6.140227 -4.3400025`

`75 1.535057 6.140227 -4.4179718`

`76 3.070113 6.140227 -4.3797034`

`77 4.605170 6.140227 -4.3073100`

`78 6.140227 6.140227 -4.2866647`

`79 7.675284 6.140227 -4.2820357`

`80 9.210340 6.140227 -4.2810151`

`81 -4.605170 7.675284 -3.7236774`

`82 -3.070113 7.675284 -4.0057353`

`83 -1.535057 7.675284 -4.2699684`

`84 0.000000 7.675284 -4.3569254`

`85 1.535057 7.675284 -4.4156364`

`86 3.070113 7.675284 -4.3842360`

`87 4.605170 7.675284 -4.2759516`

`88 6.140227 7.675284 -4.2425876`

`89 7.675284 7.675284 -4.2346514`

`90 9.210340 7.675284 -4.2328863`

`91 -4.605170 9.210340 -3.7706268`

`92 -3.070113 9.210340 -4.0406387`

`93 -1.535057 9.210340 -4.2776907`

`94 0.000000 9.210340 -4.3498230`

`95 1.535057 9.210340 -4.4095778`

`96 3.070113 9.210340 -4.3860089`

`97 4.605170 9.210340 -4.2647179`

`98 6.140227 9.210340 -4.2273624`

`99 7.675284 9.210340 -4.2183911`

`100 9.210340 9.210340 -4.2163638`

## References

- Bergmeir, Christoph, Rob J. Hyndman, and Bonsoo Koo. 2015. A Note on the Validity of Cross-Validation for Evaluating Time Series Prediction. Monash Econometrics and Business Statistics Working papers 10/15. Clayton: Department of Econometrics and Business Statistics, Monash University. [Google Scholar]
- Bonnin, François, Florent Combes, Frédéric Planchet, and Montassar Tammar. 2015. Un modèle de projection pour des contrats de retraite dans le cadre de l’orsa. Bulletin Français d’Actuariat 14: 107–29. [Google Scholar]
- Boyle, Phelim P., and Keng Seng Tan. 1997. Quasi-monte carlo methods. In International AFIR Colloquium Proceedings. Sydney: Institute of Actuaries of Australia, vol. 1, pp. 1–24. [Google Scholar]
- Caruana, Rich. 1998. Multitask learning. In Learning to Learn. Berlin: Springer, pp. 95–133. [Google Scholar]
- Chakraborty, Kanad, Kishan Mehrotra, Chilukuri K. Mohan, and Sanjay Ranka. 1992. Forecasting the behavior of multivariate time series using neural networks. Neural Networks 5: 961–70. [Google Scholar] [CrossRef]
- Cormen, Thomas H. 2009. Introduction to Algorithms. Cambridge: MIT Press. [Google Scholar]
- Dehuri, Satchidananda, and Sung-Bae Cho. 2010. A comprehensive survey on functional link neural networks and an adaptive pso–bp learning for cflnn. Neural Computing and Applications 19: 187–205. [Google Scholar] [CrossRef]
- Diebold, Francis X., and Canlin Li. 2006. Forecasting the term structure of government bond yields. Journal of Econometrics 130: 337–64. [Google Scholar] [CrossRef]
- Diebold, Francis X., and Glenn D. Rudebusch. 2013. Yield Curve Modeling and Forecasting: The Dynamic Nelson-Siegel Approach. Princeton: Princeton University Press. [Google Scholar]
- Dutang, Christophe, and Petr Savicky. 2015. Randtoolbox: Generating and Testing Random Numbers. R Package version 1.17. Auckland: R-project. [Google Scholar]
- Exterkate, Peter, Patrick J. Groenen, Christiaan Heij, and Dick van Dijk. 2016. Nonlinear forecasting with many predictors using kernel ridge regression. International Journal of Forecasting 32: 736–53. [Google Scholar] [CrossRef]
- Hochreiter, Sepp, and Jurgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9: 1735–80. [Google Scholar] [CrossRef] [PubMed]
- Hoerl, Arthur E., and Robert W. Kennard. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12: 55–67. [Google Scholar] [CrossRef]
- Hyndman, Rob J., and Yeasmin Khandakar. 2008. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27. [Google Scholar] [CrossRef]
- Joe, Stephen, and Frances Y. Kuo. 2008. Notes on Generating Sobol Sequences. Available online: http://web.maths.unsw.edu.au/~fkuo/sobol/joe-kuo-notes.pdf (accessed on 3 January 2018).
- Lütkepohl, Helmut. 2005. New Introduction to Multiple Time Series Analysis. Berlin: Springer Science & Business Media. [Google Scholar]
- Nelson, Charles R., and Andrew F. Siegel. 1987. Parsimonious modeling of yield curves. Journal of Business 60: 473–89. [Google Scholar] [CrossRef]
- Niederreiter, Harald. 1992. Random Number Generation and Quasi-Monte Carlo Methods. Philadelphia: SIAM. [Google Scholar]
- Pankratz, Alan. 2012. Forecasting with Dynamic Regression Models. Hoboken: John Wiley & Sons, vol. 935. [Google Scholar]
- Pao, Yoh-Han, Gwang-Hoon Park, and Dejan J. Sobajic. 1994. Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6: 163–80. [Google Scholar] [CrossRef]
- Penrose, Roger. 1955. A generalized inverse for matrices. In Mathematical Proceedings of the Cambridge Philosophical Society. Cambridge: Cambridge University Press, vol. 51, pp. 406–13. [Google Scholar]
- Pfaff, Bernhard. 2008. VAR, SVAR and SVEC models: Implementation within R package vars. Journal of Statistical Software 27: 1–32. [Google Scholar] [CrossRef]
- Ren, Ye, P. Suganthan, N. Srikanth, and Gehan Amaratunga. 2016. Random vector functional link network for short-term electricity load demand forecasting. Information Sciences 367: 1078–93. [Google Scholar] [CrossRef]
- Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1988. Learning internal representations by error propagation. In Neurocomputing: Foundations of Research. Cambridge: MIT Press, pp. 673–95. [Google Scholar]
- Schmidt, W. F., M. A. Kraaijveld, and R. P. Duin. 1992. Feedforward neural networks with random weights. In Proceedings: 11th IAPR International Conference on Pattern Recognition, 1992. Vol. II. Conference B: Pattern Recognition Methodology and Systems, The Hague, Netherlands, August 30–September 3. Piscataway: IEEE, pp. 1–4. [Google Scholar]
- Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Berlin: Springer. [Google Scholar]
- Zhang, Le, and P. Suganthan. 2016. A comprehensive evaluation of random vector functional link networks. Information Sciences 367: 1094–105. [Google Scholar] [CrossRef]

1 | |

2 | Diebold and Rudebusch (2013): “there are by now literally hundreds of DNS applications involving model fitting and forecasting”. |

3 | Available at https://fred.stlouisfed.org/categories/32299. |

**Table 1.**Summary of observed discount rates from Deutsche Bundesbank website, from 2002 to the end 2015.

Maturity | Min | 1st Qrt | Median | 3rd Qrt | Max |
---|---|---|---|---|---|

1 | −0.116 | 0.858 | 2.045 | 3.072 | 5.356 |

5 | 0.170 | 1.327 | 2.863 | 3.807 | 5.146 |

15 | 0.711 | 2.616 | 3.954 | 4.702 | 5.758 |

30 | 0.805 | 2.594 | 3.962 | 4.814 | 5.784 |

50 | 0.749 | 2.647 | 3.630 | 4.590 | 5.467 |

**Table 2.**Comparison of 12 months ahead out-of-sample $RMSE$ (log(Root Mean Squared Error)), for the ARIMA(AutoRegressive Integrated Moving Average), RVFL, and VAR (Vector AutoRegressive).

Method | Min. | 1st Qrt | Median | Mean | 3rd Qrt | Max. |
---|---|---|---|---|---|---|

RVFL | 0.1487 | 0.3092 | 0.4491 | 0.5041 | 0.6414 | 1.1535 |

ARIMA | 0.2089 | 0.3470 | 0.5187 | 0.6358 | 0.7516 | 5.3798 |

VAR | 0.1402 | 0.3493 | 0.5549 | 1.9522 | 0.8619 | 122.2214 |

Method | Lower Bound | Upper Bound | Mean |
---|---|---|---|

RVFL-ARIMA | −0.2116 | −0.0518 | −0.1317 |

RVFL-VAR | −3.1888 | 0.2927 | −1.4480 |

ARIMA-VAR | −2.9937 | 0.3610 | −1.3163 |

Min. | 1st Qrt | Median | Mean | 3rd Qrt | Max. | |
---|---|---|---|---|---|---|

1-year rate | −0.116 | 0.858 | 2.045 | 2.062 | 3.072 | 5.356 |

10-years rate | 0.560 | 2.221 | 3.581 | 3.322 | 4.354 | 5.570 |

20-years rate | 0.790 | 2.685 | 4.050 | 3.782 | 4.830 | 5.850 |

Correlations | 1-Year Rate | 10-Years Rate | 20-Years Rate |
---|---|---|---|

1-year rate | 1.0000 | 0.8729 | 0.8118 |

10-years rate | 0.8729 | 1.0000 | 0.9900 |

20-years rate | 0.8118 | 0.9900 | 1.0000 |

Method | Min. | 1st Qrt | Median | Mean | 3rd Qrt | Max. |
---|---|---|---|---|---|---|

RVFL | 0.1675 | 0.2906 | 0.4704 | 0.5452 | 0.6469 | 1.8410 |

VAR | 0.1382 | 0.4025 | 0.6469 | 1.0310 | 1.0750 | 13.020 |

Method | Lower Bound | Upper Bound | Mean |
---|---|---|---|

RVFL-VAR | −0.2622 | −0.7087 | −0.4854 |

**Table 8.**Descriptive statistics of St Louis Federal Reserve data for 1-year, 10-years and 30-years swap rates (in %).

Maturity | Min. | 1st Qrt | Median | Mean | 3rd Qrt | Max. |
---|---|---|---|---|---|---|

1 | 0.260 | 0.500 | 1.340 | 2.108 | 3.360 | 7.050 |

10 | 1.390 | 2.610 | 4.190 | 3.881 | 5.020 | 7.240 |

30 | 1.750 | 3.270 | 4.650 | 4.404 | 5.375 | 7.200 |

**Table 9.**Descriptive statistics for out-of-sample $RMSE$, with rolling training window = 36 months, and testing window = 36 months.

Method | Min. | 1st Qrt | Median | Mean | 3rd Qrt | Max. | Std. Dev |
---|---|---|---|---|---|---|---|

ARIMA | 0.0036 | 0.0070 | 0.0104 | 0.0149 | 0.0161 | 0.2150 | 0.0213 |

RVFL | 0.0032 | 0.0078 | 0.0115 | 0.0120 | 0.0148 | 0.0256 | 0.0055 |

Method | Lower Bound | Upper Bound | Mean |
---|---|---|---|

RVFL-ARIMA | −0.0064 | 0.0007 | −0.0028 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Moudiki, T.; Planchet, F.; Cousin, A. Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks. *Risks* **2018**, *6*, 22.
https://doi.org/10.3390/risks6010022

**AMA Style**

Moudiki T, Planchet F, Cousin A. Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks. *Risks*. 2018; 6(1):22.
https://doi.org/10.3390/risks6010022

**Chicago/Turabian Style**

Moudiki, Thierry, Frédéric Planchet, and Areski Cousin. 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks" *Risks* 6, no. 1: 22.
https://doi.org/10.3390/risks6010022