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Class ANFIS Metaheuristic Time Series Forecasting #5: Comparison with LSTM and ARIMA

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ANFIS metaheuristics comparison tutorial with LSTM and ARIMA for time series forecasting using Python. Episode 5 of the ANFIS metaheuristics time series forecasting series.

Software: Python (NumPy, statsmodels, PyTorch, Matplotlib)
Level: Advanced
Duration: ± 9 min
Series total: 59 min · 6 videos
Published: October 25, 2026

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Class ANFIS Metaheuristic Time Series Forecasting #5: Comparison with LSTM and ARIMA: Cover: Metaheuristic ANFIS Time Series Forecasting Class #5: Comparison with LSTM and ARIMA

Class ANFIS Metaheuristic Time Series Forecasting #5: Comparison with LSTM and ARIMA: Before you start, steps list, and Step 1

Class ANFIS Metaheuristic Time Series Forecasting #5: Comparison with LSTM and ARIMA: Step 2: LSTM with early stopping

Class ANFIS Metaheuristic Time Series Forecasting #5: Comparison with LSTM and ARIMA: Step 3: Three models on the test data

Class ANFIS Metaheuristic Time Series Forecasting #5: Comparison with LSTM and ARIMA: Step 4: Grafik ramalan

Class ANFIS Metaheuristic Time Series Forecasting #5: Comparison with LSTM and ARIMA: Done: Final Result and Further Practice

Cover: Metaheuristic ANFIS Time Series Forecasting Class #5: Comparison with LSTM and ARIMA

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Transcript

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Introduction

Here is the final result, Anfis Meta Heuristics is compared with Arima and LSTM. On the Mickey Glass Chaos Series, Anfis is 12 times more accurate than LSTM. However, on the short sunspot series, the simplest Arima actually wins. Let's start. Hello, welcome to episode five of the Anfis Meta Heuristics series for time series forecasting. A new model only makes sense when compared with a strong benchmark. We use two of the most common in journals, Arima from Classical Statistics and LSTM from Deep Learning, on the exact same test data.

Step 1: ARIMA rolling origin

Create a benchmark .pi file. The Arima forecasting function trains Arima on the training data, order 4, 0, 2 for Mickey Glass and R9 for sunspot according to its cycle. Then, with fixed parameters, the model is run across the entire series, and from each origin point in the test data, we request dynamic forward forecasts, so that it is comparable with Anfis.

Step 2: LSTM with early stopping

The LSTM forecasting class has one LSTM layer with 32 units and a linear layer. The LSTM forecasting function orders the input lags from the oldest as a sequence, trains 400 epochs with Adam, and saves the weights with the best RMSI validation, using early stopping technique to avoid overfitting. The main part loads the premise parameters from the previous episode and selects PSO or GE based on RMSI validation, not the test data.

Step 3: Three models on the test data

Then all three models forecast the test data, RMSI is printed, and all forecasts are saved to the NPZ file. Run On Mickey Glass, Anfis PSO achieves RMSI 0,0029, LSTM 0,0339, and Arima 0,0485. Anfis is 12 times more accurate than LSTM and 17 times more accurate than Arima. On Sunspot, Arima 0,1128, Anfis Ga 0,1303, and LSTM 0,1335. Continue with the forecast graph.

Step 4: Forecasting graph

Let's see the graph. On Mickey Glass, the Anfis line sticks closely to the actual data, LSTM slightly deviates at the peaks, and Arima lags behind on sharp changes because it cannot capture non-linear dynamics. On Sunspot, all three are similar and all struggle at the highest peak. The lesson is that non-linear models excel when the data is rich and the non-linear pattern is strong, but on short data, simple models with few parameters can be more robust.

Done!

Done. We have fairly compared the metaheuristic Anfis with Arima and LSTM. But does the slight difference in Sunspot mean anything? In the last episode, we answer with comprehensive metrics and the Daibold Mariano test. Currently working on a thesis, dissertation, or research in the field of forecasting and artificial intelligence? Cerdas Inovasi Digital helps implement forecasting models. From the Fazi Sugeno and Anfis systems, parameter optimization with PSO and genetic algorithms, up to comparisons with LSTM and Arima as well as the Daibold Mariano test, and 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, datasets, 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 the single class 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

0:43Step 1: ARIMA rolling origin

3:11Step 2: LSTM with early stopping

5:05Step 3: Three models on the test data

6:32Step 4: Forecasting graph

7:42Done!

Ask the class material

My notes

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Recommended classes for you

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Transcript

0:03Introduction

0:43Step 1: ARIMA rolling origin

3:11Step 2: LSTM with early stopping

5:05Step 3: Three models on the test data

6:32Step 4: Forecasting graph

7:42Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: ARIMA rolling origin

Step 2: LSTM with early stopping

Step 3: Three models on the test data

Step 4: Forecasting graph

Done!

Introduction

Step 1: ARIMA rolling origin

Step 2: LSTM with early stopping

Step 3: Three models on the test data

Step 4: Forecasting graph

Done!