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32 changes: 30 additions & 2 deletions 13_time_series/13_time_series.jl
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
### A Pluto.jl notebook ###
# v0.12.20
# v0.12.21

using Markdown
using InteractiveUtils
Expand All @@ -10,6 +10,13 @@ using Plots
# ╔═╡ c470f03c-3a33-11eb-3929-7f8c45b6fdcd
using Optim

# ╔═╡ 9ad5e3a0-8be2-11eb-0ca1-b9a933682476
md"### To do list

We are currently working on:

";

# ╔═╡ 01cbe438-34d1-11eb-087b-b5294ea7b996
md"# Predicting the future"

Expand Down Expand Up @@ -795,13 +802,33 @@ md"Well, good! We started this chapter not knowing how to tackle time series for

As a final summary, when dealing with a time series it is very important to be able to define if it has any latent variables such as trend or seasonality. Once we can find that underlying information, we will be able to generate forecasts with confidence. We just need to look deeper.

### Bibliography
### Summary

In this chapter, we learned the basic foundations of time series analysis.
We defined what a time series is and delved into a particular method, the exponential smoothing, that allows us to take into account the most distant values of our data.
Finally, we explained more complex versions of the method and used them to make predictions in different kinds of scenarios.
When the processes followed a marked tendency, we used the trend method and the damped trend method to make long term predictions.
When the process exhibited seasonal trends, we utilized the Holt-Winters’ seasonal method.

### References

- [Forecasting: Principles and Practice, Chap 7](https://otexts.com/fpp2/expsmooth.html)

"

# ╔═╡ 8ade9490-8b6c-11eb-30f5-89ca2fab1b48
md" ### Give us feedback


This book is currently in a beta version. We are looking forward to getting feedback and criticism:
* Submit a GitHub issue **[here](https://github.com/unbalancedparentheses/data_science_in_julia_for_hackers/issues)**.
* Mail us to **[email protected]**

Thank you!
"

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Expand Down Expand Up @@ -866,3 +893,4 @@ As a final summary, when dealing with a time series it is very important to be a
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