Time_Series_Prediction

KO | EN

gitlite — search

#python #react #ai #llm #deep-learning #docker #java #windows #awesome #cli #kubernetes #awesome-list #security #ios

Time_Series_Prediction

(★ 9)

ARIMA model, LSTM neural network and DMD algorithm

Time_Series_Prediction 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## Intro

This repo aims at presenting different techniques for making time series prediction.

## Approach 1: ARIMA models & LSTM neural networks

- ARIMA_and_LSTM_models.ipynb
- lstm.py

Dataset: https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-series-data

Each datapoint is a daily measurement of 4 features describing climate conditions. We first deal with outliers and the stationarity issue (use ADF test to detect and Differencing technique) and then build one ARIMA model for each feature and one multifeature LSTM model equipped with trend and seasonality features to tackle underfitting. We note that the current LSTM model is not fine tuned (*to do*). However, it still serves our comparison purposes between ARIMA and LSTM.

### Performance results:
We note that the LSTM outperforms ARIMA in all features.  
        
            | ----------- | ---- MAE ---- |  ----RMSE----- |    
            | feature     | ARIMA   LSTM  |  ARIMA   LSTM  |
            | meantemp    |  5.4    3.87  |   6.24   4.47  |
            | humidity    |  16.55  16.39 |   21.02  18.82 |
            | wind speed  |  3.03   2.94  |   3.92   3.74  |
            | meanpressure|  6.57   5.17  |   8.31   6.67  |
            ------------------------------------------------
            where MAE = Mean Absolute Error, RMSE = sqRt of Mean Squared Error

### A short recap on theory basics:
*(more detail to be added)*
   <p align="center">
     <img src="https://github.com/vggls/Time_Series_Prediction/assets/55101427/7ba011ff-68d4-43da-babe-b14f204d302d.png" height="650" width="500" />
     <img src="https://github.com/vggls/Time_Series_Prediction/assets/55101427/07720097-63b5-457a-b49d-217c9e734d13.png" height="360" width="500" />
   </p>

## Approach 2: Hankel Matrix and DMD algorithm

- HankelMatrix_and_DMDalg__model.ipynb

Dataset: We consider a 35-year (Rhine) river level dataset where each time observation describes the water level at that time for multiple cities located across the river. The task is to build a model that predicts the river's future water level for each city. The dataset cannot be made public.

In this approach we see the time data points as snapshots of an underlying non-linear dynamical system. First we build the time-delayed Hankel matrix in order to embed the system into a space where it behaves  similar to a linear system. We conclude that by observing the time-evolution of the principal components of the Hankel matrix.
Then, taking advantage of the "linear" behaviour of our system, we refer to the Dynamic Mode Decomposition (DMD) of the Hankel matrix in order to
determine the DMD nodes of the system and make predictions about future behaviour.

Useful Links:
  - Hankel matrix : https://en.wikipedia.org/wiki/Hankel_matrix
  - DMD : https://en.wikipedia.org/wiki/Dynamic_mode_decomposition

Time_Series_Prediction

Time_Series_Prediction 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Time_Series_Prediction

Time_Series_Prediction 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README