Hi This dataset contains the responses of a gas multisensor device deployed on the field in an Italian city. The dataset contains 9,358 instances of hourly averaged responses from an array of several metal oxide chemical sen- sors embedded in an Air Quality Chemical Multisensor Device. The device was located on the field in a significantly polluted area, at road level, within an Italian city. Data were recorded from March 2004 to April 2005 (one year). Ground Truth hourly averaged concentrations for Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) are pro- vided, together with information on weather conditions. Missing values are tagged with -200 value.
- Im trying to split the data into two datasets: a training dataset and a test dataset. The training dataset should incude the first 7,111 observations (until the last observation of 2004). The aim will be to use the training dataset to forecast the value of NOx concentration at 9am in January 2005. Therefore, split the original dataset into a training and a test dataset. The test dataset should include the 31 observations at 9am every day in January 2005.
- Fit a statistical model to the training data and use it to forecast the NOx concentra- tion at 9am every day in January 2005
Please guide me, I'm unable to proceed
install.packages("fma")
library(fma)
install.packages("xts")
library(xts)install.packages("dplyr")
library(dplyr) airq
Time series analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals. The data is considered in three types: ... Cross-sectional data: Data of one or more variables, collected at the same point in time
Enter time series. A time series is simply a series of data points ordered in time. In a time series, time is often the independent variable and the goal is usually to make a forecast for the future.
However, there are other aspects that come into play when dealing with time series.
Is it stationary?
Is there a seasonality?
Is the target variable autocorrelated?
In this post, I will introduce different characteristics of time series and how we can model them to obtain accurate (as much as possible) forecasts.
Time series data is data that is collected at different points in time. This is opposed to cross-sectional data which observes individuals, companies, etc. at a single point in time.
Because data points in time series are collected at adjacent time periods there is potential for correlation between observations. This is one of the features that distinguishes time series data from cross-sectional data.
Time series data can be found in economics, social sciences, finance, epidemiology, and the physical sciences.
The statistical characteristics of time series data often violate the assumptions of conventional statistical methods. Because of this, analyzing time series data requires a unique set of tools and methods, collectively known as time series analysis.
I would have suggested that you make use of Microsoft Excel or the GenStat to solve this problem.
Go through this video. It would really help you to solve the problem.
https://www.youtube.com/watch?v=gj4L2isnOf8
There are two methods in time series analysis, parametric and non-parametric. In both ways, you need to identify the nature of the phenomenon represented by the sequence of observations, and forecast/predict future values of the time series variable.
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