All classes ANFIS Metaheuristics Time Series Forecasting Syllabus · Episode 4 of 6

Class ANFIS Metaheuristic Time Series Forecasting #4: Optimizing ANFIS Parameters with PSO and Genetic Algorithm

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Tutorial on optimizing ANFIS parameters using PSO and genetic algorithm with Python. Episode 4 of the ANFIS metaheuristics time series forecasting series.

Software: Python (NumPy, statsmodels, PyTorch, Matplotlib)
Level: Advanced
Duration: ± 10 min
Series total: 59 min · 6 videos
Published: October 25, 2026

Episode 4 of 6 · view syllabus

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Introduction

This is the final result, the ANVIS membership function parameters found by PSO and genetic algorithm. On Mackey Glass, ANVIS is 22% more accurate than gradient descent. On sunspot results are more varied, an important lesson for research. Let's get started. Hello, welcome to episode four of the ANVIS Meta Heuristics series for time series forecasting. Gradient descent can get stuck in local minima and depends on learning rate. Meta Heuristics like PSO and genetic algorithm search globally without gradient. Today we combine it with ANVIS intelligently.

Step 1: Fitness, PSO & GA

Create file optimasi underscore anvis.py The ANVIS fitness function receives a 16 parameter vector premis, builds ANVIS, calculates the consequent with list scores on 80% training data, then returns RMSI on the remaining 20% as validation data. So Meta Heuristics only searches for 16 parameter premis, and 80 consequents are solved analytically. The PSO function is Standard Particles Swarm Optimization with inertia 0.7. The GA function is real-number genetic algorithm, tournament selection, BLX-alpha crossover that produces offspring around the range of the two parents, gaussian mutation, and elitism of the two best individuals. For each series, we train ANVIS with gradient descent as a reference,

Step 2: Optimizing both series

then run PSO and GA with 20 individuals for 40 iterations. Center bounds from -0.2 to 1.2 and width from 0.05 to 1. The best parameters are saved to a JSON file for the next episode. Run. On Mackie Glass, ANVIS PSO achieves RMSI UG 0,0029, ANVIS GA 0,0025, and gradient descent 0,0032. On Sunspot, ANVIS PSO 0,1251, slightly better than gradient descent 0,1265, but ANVIS GA 0,1303, slightly worse although RMSI validation is the smallest. Continue with the convergence fitness graph and RMSI UG comparison.

Step 3: Convergence & Test RMSE

Let's see the graph. The fitness curve drops quickly in the first 10 iterations. On Sunspot, the model with the best validation is not always the best on the test data, because the validation data is small and the test data contains higher cycles. This is the reason why honest research must choose a model based on validation, not test data.

Done!

Done. We have optimized the premise parameters of ANVIS with PSO and genetic algorithm, with consequents still solved list scores, and analyzed the results honestly. In the next episode, we will compare with LSTM and ARIMA. Currently working on a thesis, dissertation, or research in the field of forecasting and artificial intelligence? Cerdas Inovasi Digital helps implement forecasting models, from Sugeno fuzzy systems and ANVIS, parameter optimization with PSO and genetic algorithms, up to LSTM and ARIMA comparisons and Diebold-Mariano tests, as well as opening Python programming courses for engineering. Want to master the entire series, from chaotic time series to statistical evaluation? All six episodes, complete with step-by-step PDF guides, data sets, and source code, are available to premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy single classes with lifetime access. Subscribe now at the premium page cerdasinovasidigital.com. Contact our WhatsApp, or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:46Step 1: Fitness, PSO & GA

4:37Step 2: Optimizing both series

7:04Step 3: Convergence & Test RMSE

8:30Done!

Ask the class material

My notes

Questions & answers

Your instructor

Recommended classes for you

Need more than a video?

Transcript

0:03Introduction

0:46Step 1: Fitness, PSO & GA

4:37Step 2: Optimizing both series

7:04Step 3: Convergence & Test RMSE

8:30Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Fitness, PSO & GA

Step 2: Optimizing both series

Step 3: Convergence & Test RMSE

Done!

Introduction

Step 1: Fitness, PSO & GA

Step 2: Optimizing both series

Step 3: Convergence & Test RMSE

Done!