When we make a model for forecasting purposes in time series analysis, we require a stationary time series for better prediction. So the first step to work on modeling is to make a time series stationary. Testing for stationarity is a frequently used activity in autoregressive modeling. We can perform various tests like the KPSS, Phillips–Perron, and Augmented Dickey-Fuller. This article is more focused on the Dickey-Fuller test. The article will see the mathematics behind the test and how we can implement it in a time series.
Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.
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A Complete Introduction To Time Series Analysis (with R):: Tests for Stationarity
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This tutorial was supposed to be published last week. Except I couldn’t get a working (and decent) model ready in time to write an article about it. In fact, I’ve had to spend 2 days on the code to wrangle some semblance of useful and legible output from it.
But I’m not mad at it (now). This is the aim of my challenge here and truthfully I was getting rather tired of solving all the previous classification tasks in a row. And the good news is I’ve learned how to model the data in a suitable format for processing, conducting exploratory data analysis on time-series data and building a good (the best I could come up with, like, after 2 days) model.
So I’ve also made a meme to commemorate my journey. I promise the tutorial is right on the other side of it.
Yes, I made a meme of my own code.
_About the Dataset: __The Gas Sensor Array Dataset, download from here**, _**consists of 8 sensor readings all set to detect concentration levels of a mixture of Ethylene gas with either Methane or Carbon Monoxide. The concentration levels are constantly changing with time and the sensors record this information.
Regression is one other possible type of solution that can be implemented for this dataset, but I deliberately chose to build a multivariate time-series model to familiarize myself with time-series forecasting problems and also to set more of a challenge to myself.
Time-Series data continuosuly varies with time. There may be one variable that does so (univariate), or multiple variables that vary with time (multivariate) in a given dataset.
Here, there are 11 feature variables in total; 8 sensor readings (time-dependent), Temperature, Relative Humidity and the Time (stamp) at which the recordings were observed.
As with most datasets in the UCI Machine Learning Repository, you will have to spend time cleaning up the flat files, converting them to a CSV format and insert the column headers at the top.
If this sounds exhausting to you, you can simply downloadone such file I’ve already prepped.
his is going to be a long tutorial with explanations liberally littered here and there, in order to explain concepts that most beginners might not be knowing. So in advance, thank you for your patience and I’ll keep the explanations to the point and as short as possible.
Before heading into the data preprocessing part, it is important to visualize what variables are changing with time and how they are changing (trends) with time. Here’s how.
Time Series Data Plot
# Gas Sensing Array Forecast with VAR model # Importing libraries import numpy as np, pandas as pd import matplotlib.pyplot as plt, seaborn as sb # Importing Dataset df = pd.read_csv("dataset.csv") ds = df.drop(['Time'], axis = 1) # Visualize the trends in data sb.set_style('darkgrid') ds.plot(kind = 'line', legend = 'reverse', title = 'Visualizing Sensor Array Time-Series') plt.legend(loc = 'upper right', shadow = True, bbox_to_anchor = (1.35, 0.8)) plt.show() # Dropping Temperature & Relative Humidity as they do not change with Time ds.drop(['Temperature','Rel_Humidity'], axis = 1, inplace = True) # Again Visualizing the time-series data sb.set_style('darkgrid') ds.plot(kind = 'line', legend = 'reverse', title = 'Visualizing Sensor Array Time-Series') plt.legend(loc = 'upper right', shadow = True, bbox_to_anchor = (1.35, 0.8)) plt.show() view raw gsr_data_prepocessing.py hosted with ❤ by GitHub
It is evident that the ‘Temperature’ and ‘Relative Humidity’ variables do not really change with time at all. Therefore I have dropped the columns; time, temperature and rel_humidity from the dataset, to ensure that it only contains pure, time-series data.
Non-stationary data has trends that are present in the data. We will have to eliminate this property because the Vector Autoregression (VAR) model, requires the data to be stationary.
A Stationary series is one whose mean and variance do not change with time.
One of the ways to check for stationarity is the ADF test. The ADF test has to be implemented for all the 8 sensor readings column. We’ll also split the data into train & test subsets.
#multivariate-analysis #time-series-forecasting #data-science #machine-learning #time-series-analysis #data analysis
In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting.
What is Time Series Analysis?
In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.
Here some examples where time series is used.
Components of time series :
Stationarity of a time series:
A series is said to be “strictly stationary” if the marginal distribution of Y at time t[p(Yt)] is the same as at any other point in time. This implies that the mean, variance, and covariance of the series Yt are time-invariant.
However, a series said to be “weakly stationary” or “covariance stationary” if mean and variance are constant and covariance of two-point Cov(Y1, Y1+k)=Cov(Y2, Y2+k)=const, which depends only on lag k but do not depend on time explicitly.
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Whether you want to predict the temperature of the environment or estimate electricity consumption for the next few months, stationarity of time series models is necessary if you want the forecasting model to work well. Most of the time series models are non-stationary either it gives an upward or downward trend or seasonal effects.
The main aim of this article is to discuss the methods for checking the stationarity in time series data. We will do the experiments on the time series data to check this.
In the above plot we observe that data do not change over time. It won’t show any trend or seasonal effect. Mean and variance remains constant over time.
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