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FireAngel HW1-R Heat Alarm , White

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Before we predict values in the future, we’ll need to create a fit to the data. In the most basic method, we can simply call the Holt-Winters function and let R figure out the tuning parameters on it’s own. We also have the opportunity to tune the fit manually by setting tuning variables: Should we get into the case where our time series uses hours/minutes/seconds or time zones, we’d need to convert into a POSIXct, but that’s not needed here. Converting to a time series If you live in Scotland find out more about the law in your area on our blog post here. (link to homeowner Scottish regulations blog)

As we look at the decomposition components, we can visually see how they can add up to our “observed” value (our real values). It’s also very important to inspect the scales of each section to see which one is more dominant. For example, if “random” has a range significantly larger than seasonal or trend, this data is going to be very challenging to accurately forecast later on. Fitting with Holt-Winters Please complete the following tasks using the appropriate instructions for your computer (i.e. Mac or Windows). For Macs: Every file you have on your computer can be referenced by a file path. If you open Finder which should be on your toolbar. It will look something like this, where your home directory will be labeled differently than the one shown here and you may have different folders listed. The panel in the upper right contains your workspace as well as a history of the commands that you’ve previously entered. Any plots that you generate will show up in the panel in the lower right corner. Here we can make two fits, and plot them versus the raw data to see the quality of the fits. I highly recommend playing with the alpha, beta, and gamma values to see how the fit changes. HW1 <- HoltWinters(dfts) # Custom HoltWinters fittingThis is a very simple step, but also one of the easiest to forget. Before we do anything regarding forecasting, we need to tell R that this data is a time series. To do this, we make a time series object. There are two critical inputs we must give the function — frequency and start. The goal of this homework is to introduce you to R and RStudio, which you’ll be using throughout the course both to learn the statistical concepts discussed in the texbook and also to analyze real data and come to informed conclusions. To straighten out which is which: R is the name of the programming language itself and RStudio is a convenient environment for writing and running R code. The panel on the left is where the action happens. It’s called the console. Everytime you launch RStudio, it will have the same text at the top of the console telling you the version of R that you’re running. Below that information is the prompt, >. As its name suggests, this prompt is really a request, a request for a command. Initially, interacting with R is all about typing commands and interpreting the output. These commands and their syntax have evolved over decades (literally) and now provide what many users feel is a fairly natural way to access data and organize, describe, and invoke statistical computations.

beta: the “trend value”. Higher beta means the trend slope is more dependent on recent trend slopes. gamma: the “seasonal component”. Higher gamma puts more weighting on the most recent seasonal cycles. Smoke Alarms: To offer high protection FireAngel advise a smoke alarm within the circulation area of each floor of the property, as well as within the main habitable areas such as bedrooms and living room. It’s important to remember that the sensors in all smoke alarms deteriorate after time, at FireAngel we recommend that smoke alarms are replaced every 10 years.

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R should now be in your “Applications” folder which can again be found on the left hand side of your Finder window. This will start an installer for RStudio. Unless you prefer otherwise, choose the defaults for the installation process. This will take a few minutes.

Immediately we can “decompose” the time series — which in this case means separating out the 3 main components that make up the time series: Optical smoke detection technology – less prone to nuisance alarms than traditional ionisation sensors We suggest you create a new folder for every assignment in this class where all the files for that assignment are kept. Use the directions in step 2 to create a folder for homework 1 in your BTRY 3010 folder.Heat Alarms (Kitchen Alarms): Heat alarms are the only alarm suitable for use within the kitchen as they’re not affected by cooking fumes like traditional smoke alarms. Over 60 Percent of fires start in the kitchen, with cooking and faulty white goods (fridges and tumble driers) being the main causes. FireAngel recommend a heat alarm is fitted in the kitchen, but you can also fit additional heat alarms in the garage and loft to protect these areas. The instructions below are for students who have personal computers that they will use for this course. If you are going to use lab computers, create a “BTRY 3010” folder on your thumb drive. Then within this folder, create a folder for homework 1. It is best if every week you create new folders to store all of the files for the homework and lab for that week. This will start the installer for R. Go through the steps to start the installation. When you have choices, unless you prefer otherwise, choose the defaults. This will take a few minutes.

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