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Build an Autoregressive and Moving Average Time Series Model

Welcome to time series analysis! We explore this project at a much deeper level to understand and predict the IoT sensor readings. It is mainly to investigate how the sensor data can be used to analyze through Moving Average and Autoregressive models. The models above can help us find hidden patterns and predict future readings.

Project Overview

This project starts by cleaning and preparing the IoT sensor data so that we can analyze it. We then proceed to build many models: Moving Average models (MA(1), MA(2)) and then Autoregressive models (AR(1), AR(2), AR(3), AR(4)). Now that we have these models, we can understand the relationship between past and future values.

We then determine how well each model describes the predictions with Root Mean Squared Error (RMSE), a common way to gauge accuracy in forecasts. This makes things more insightful, so we bring in visualizations such as autocorrelation plots and rolling average plots. They help us see how sensor readings behave over time and how different models perform. By the end of this project, we will have a good idea of which model fits the data best and which one can confidently predict from historical sensor data.

Prerequisites

  • Knowledge of time series analysis and some basic concepts, such as stationarity and autocorrelation.
  • Python and libraries including Pandas, NumPy, and Matplotlib.
  • Knowledge of machine learning models such as Moving Average (MA) and Autoregressive (AR) models.
  • Experience with model performance metric computation including Root Mean Squared Error (RMSE).
  • The knowledge on how to preprocess clean data to analyze time series data.
  • Visualization tools for time series, for example, autocorrelation plots and rolling averages.
  • An Augmented Dickey-Fuller (ADF) test for stationarity

Approach

First, we clean up the IoT sensor data to prepare it for analysis and modeling. Then we dive into different time series models, starting with Moving Average (MA) models and then Autoregressive (AR) models with varying lags. These models aim to capture the dependence of future values of the data on the past. To check for stationarity we use the Augmented Dickey-Fuller (ADF) test, and to smoothen the data we use rolling averages. For each model, we compute the Root Mean Squared Error (RMSE) so that we can evaluate the accuracy of the predictions of each model. Autocorrelation plots give us visuals of how the data is related to each other. We finally pick the most accurate model using RMSE and then use the one we picked to make future predictions to gain valuable insights into IoT sensor behavior.

Workflow and Methodology

  • Load and prepare the IoT sensor data for further analysis.
  • Apply the Augmented Dickey-Fuller (ADF) test for stationarity.
  • Construct various time series models (MA and AR) under different lags.
  • Train each of the models with the prepared data.
  • Compute a fitted value for each model.
  • Calculate the Root Mean Square Error (RMSE) for each model to evaluate model performance.
  • Visualize the autocorrelation plots and rolling average of data.
  • Compare the RMSE value for every model to identify the best among the models about performance.
  • Have predictions based on the selected model.

Data Collection and Preparation

Data Collection:

In this project, we collected the dataset from a public repository. If you are looking to work on a real-world problem, you can get these kinds of datasets from publicly available repositories such as Kaggle, UCI Machine Learning Repository, or company-specific data. We will provide the dataset in this project so that you can work on the same dataset.

Data Preparation Workflow:

  • Import the dataset and inspect it for any missing values or inconsistencies.
  • Convert the date column to a proper timestamp format for time series analysis.
  • Set the time column as the index to allow for time-based operations.
  • Handle missing values by using methods like forward filling or backward filling.
  • Visualize the time series data to identify trends, seasonality, and noise.

Code Explanation

STEP 1:

Mounting of Google Drive

This code mounts your Google Drive into the Colab environment so that you can access files stored in your drive. Your Google Drive is made accessible under the /content/drive path.

from google.colab import drive
drive.mount('/content/drive')

Ignoring Warnings

This code will suppress all the warnings, thus preventing them from being displayed during execution. This ensures that the output is clean while running the program.

# ignore warnings
import warnings
warnings.filterwarnings('ignore')

Required Library Installation

This code is meant to install the required libraries for Python such as: plotting through matplotlib, data manipulation by pandas, performing statistical modeling with statsmodels, seaborn for visualizing the data, scipy computes scientific in addition to mathematical problems, provides numerical work through numpy, and the last one is machine learning with scikit-learn.

!pip install matplotlib
!pip install pandas
!pip install statsmodels
!pip install seaborn
!pip install scipy
!pip install numpy
!pip install scikit-learn

Importing Required Libraries for Time Series Analysis

All the libraries have been imported to perform time series analysis, including pandas, numpy, statsmodels, and matplotlib. All the libraries support functions like seasonal decomposition, statistical tests, ARIMA modeling, and graphical representation of autocorrelation functions for time series data analysis.

#importing all required libraries
import pandas as pd
import numpy as np
from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller,kpss
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.ar_model import AutoReg
from sklearn.metrics import mean_squared_error
from pandas.plotting import autocorrelation_plot
import scipy.stats
import pylab
from statsmodels.tsa.stattools import kpss
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from statsmodels.tsa.seasonal import seasonal_decompose

STEP 2:

Loading Data and Checking Shape

This code loads the CSV file. After loading the dataset it prints the dataset’s shape to check the number of rows and columns. The %time magic command in the notebook records the time taken to perform the task.

Code Editor