Eksik GSYH’yi tahmin etme

Sağdaki grafik, en üstte orijinal GSYH verilerini, ardından eksikliği ele almak için farklı tekniklerle oluşturduğun üç xts nesnesini gösteriyor: locf, nocb ve approx. Hangi tekniği kullanacağına ilişkin kararın, verinin neyi temsil ettiği ve eksik olmayan veriden hangi ilişkileri çıkarabildiğin de dahil olmak üzere koşullar tarafından belirlenmelidir.

Veriyi anlama biçimine dayanarak, bu bağlamda hangi yöntem daha mantıklı?

Bu egzersiz

Vaka Çalışması: R ile Şehir Zaman Serisi Verilerini Analiz Etme

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Bu egzersiz

Vaka Çalışması: R ile Şehir Zaman Serisi Verilerini Analiz Etme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

You've been hired to understand the travel needs of tourists visiting the Boston area. As your first assignment on the job, you'll practice the skills you've learned for time series data manipulation in R by exploring data on flights arriving at Boston's Logan International Airport (BOS) using xts & zoo.

Exercise 1: Review xts fundamentals Exercise 2: Identify the time series Exercise 3: Flight data Exercise 4: Pick out the xts object Exercise 5: Encoding your flight data Exercise 6: Manipulating and visualizing your data Exercise 7: Exploring your flight data Exercise 8: Visualize flight data Exercise 9: Calculate time series trends Exercise 10: Saving and exporting xts objects Exercise 11: Assessing flight trends Exercise 12: Saving time - I Exercise 13: Saving time - II

In this chapter, you'll expand your time series data library to include weather data in the Boston area. Before you can conduct any analysis, you'll need to do some data manipulation, including merging multiple xts objects and isolating certain periods of the data. It's a great opportunity for more practice!

Exercise 1: Merging time series data by row Exercise 2: Exploring temperature data Exercise 3: Next steps - I Exercise 4: Merging using rbind()Exercise 5: Visualizing Boston winters Exercise 6: Merging time series data by column Exercise 7: Subsetting and adjusting periodicity Exercise 8: Generating a monthly average Exercise 9: Using merge() and plotting over time Exercise 10: Are flight delays related to temperature?Exercise 11: Time series data workflow Exercise 12: Next steps - II Exercise 13: Expanding your data Exercise 14: Are flight delays related to visibility or wind?

Now it's time to go further afield. In addition to flight delays, your client is interested in how Boston's tourism industry is affected by economic trends. You'll need to manipulate some time series data on economic indicators, including GDP per capita and unemployment in the United States in general and Massachusetts (MA) in particular.

Exercise 1: Eksik değerlerle başa çıkma Exercise 2: Ekonomik verileri keşfetme Exercise 3: Eksik verileri değiştir - I Exercise 4: Eksik verileri değiştir - II Exercise 5: Eksik GSYH’yi tahmin etme

Geçerli Egzersiz

Exercise 6: Geciktirme ve fark alma Exercise 7: İşsizlik verilerini inceleme Exercise 8: İşsizliği ötelemek Exercise 9: İşsizlikte fark alma Exercise 10: Kayan (rolling) işlevler Exercise 11: GSYİH verisine ayrık bir yuvarlanan toplam ekle Exercise 12: İşsizlik verisine sürekli bir dönen ortalama ekle Exercise 13: MA işsizlik verilerini dönüştürme

Having exhausted other options, your client now believes Boston's tourism industry must be related to the success of local sports teams. In your final task on this project, your supervisor has asked you to assemble some time series data on Boston's sports teams over the past few years.

Exercise 1: Advanced features of xts Exercise 2: Encoding and plotting Red Sox data Exercise 3: Calculate a closing average Exercise 4: Calculate and plot a seasonal average Exercise 5: Calculate and plot a rolling average Exercise 6: Indexing commands in xts Exercise 7: Extract weekend games Exercise 8: Calculate a rolling average across all sports Exercise 9: Viewing sports trends Exercise 10: Congratulations