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David Andrés 🤖📈🐍

Nov 9 • 6 tweets • 3 min read

Time Series analysis and forecasting is a really valuable skill to have in #DataScience.

Here is WHY🧵👇

:one: All companies are interested in making money. Time series is really powerful in #finance! 📈📉

There will always be demand for someone who can analyse and forecast financial data. Plus it can bring you a lot of money if you can increase the profit of a company! 💰💰💰

:two: There are multiple applications for Time Series: forecasting sales, unemployment rate, COVID cases, petrol price, temperatures...

There is a demand for Data Scientists with this skill everywhere! You are not restricted to a particular field 🔭💊🧬📡 or location 🇪🇺🇺🇸🇮🇳!

:three: It can help you make wise investments and therefore money! 🤑🤑

You can make your own forecastings on stocks 📈, real estate 🏡, and even #Bitcoin!

These are just some reasons. Knowing Time Series will make you a better Data Scientist.

Let me know if you want a second part. I will bring you more applications and reasons to learn Time Series 😉

@daansan_ml

So far we have only talked about traditional methods, but soon we will start with the #MachineLearning and #DeepLearning side of it.

Don't forget to Follow me on @daansan_ml and Retweet not to miss out!!

https://twitter.com/daansan_ml/status/1590388768409722880?s=20&t=Zqccmn3pCv4hNXB02r2SOA

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David Andrés 🤖📈🐍

@daansan_ml

Nov 11

We have so many combinations of parameters for our ARIMA model!! 🤯

Find out here how to choose the best one 🧵👇

#TimeSeries #DataScience

🔎 The objective of our ARIMA model is to maximize the Log-Likelihood (ℓ). Which indicates how well the model fits our data.

👎 However, this can lead to an over-complicated model that overfits the training data. We want it to be also valid for unseen data!!

What can we do? 👇

We can use the AIC which stands for Akaike Information Criterion.

It is an estimator of the relative quality of a model for a given set of data.

Read 8 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 10

Is the #Bitcoin price a stationary times series? 🤔

Check 👇 how to find it out!

#DataScience #MachineLearning

We know that stationary means that the mean and variance of the time series data do not vary across time.

To be sure of that we can perform the Augmented Dickey-Fuller test.

If you are familiar with #statistics here you have the hypotheses for this test:

H0️⃣ (Null hypothesis) = Time series non-stationary

H1️⃣ (Alternative hypothesis) = Time series is stationary

After the test, we will pay attention to the "p-value".

Read 11 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 9

ARMA cannot be used with #Bitcoin prices!

Here is why 👇👇

#timeseries #datascience

You can't use an ARMA model with prices because for using this kind of model you need a stationary time series!

What does it mean? 👇

⭐️A time series is stationary if its mean and variance do not vary across time.

⭐️It is considered non-stationary if there is a strong trend or seasonality observed from the data.

Read 11 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 8

Use ARIMA to forecast #BTC price!

ACF and PACF graphs can help, here is how👇👇👇

The lag 0 (no lagged time series) is the original time series, so its correlation will always be 1. This lag is redundant and it can be ignored.

Values within the blue-shaded area are statistically non-different from 0 (no correlation).

https://twitter.com/daansan_ml/status/1588226047626989568

Let's see how these graphs can help us get a first estimate of the parameters "p" and "q". 👇👇👇

But if you don't remember what ACF and PACF or lags mean, have a look at yesterday's thread first!

https://twitter.com/daansan_ml/status/1588226047626989568

Read 10 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 7

Let's continue with the #TimeSeries forecasting 😀

We want to use an #ARIMA to forecast the #BTC price. But... how can we select its parameters?

#machinelearning #python #datascience

Let's first start with "p" and "q".

For this, we need to check how correlated the time series is with lagged versions of itself.

The original time series will be denoted as 0. A 1-timestep lagged version will be referred to as 1. And so on...

The correlation of the original time series with the lag 0 (no lagged), will always be equal to 1, since they are the same time series.

The questions are...
• What is the correlation of the original time series with lag 1?
• What about lag 2?
• And 3, 4, 5...?

Read 10 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 3

I have started the #TimeSeries project about predicting Bitcoin #BTC price.

First I am going to try a traditional method: #ARIMA. Will it work? 🤔

If you don't know what it is 🧵👇

#datascience #artificialintelligence #machinelearning

It is composed of 3 elements:
1️⃣ AR: Auto-Regressive
2️⃣ I: Integrated
3️⃣ MA: Moving Average

Let's introduce them... 👇

1️⃣ [ AR ]-IMA

🔎It takes into account "p" previous values, in my case previous #BTC prices.

🔮To make the prediction it relies on the previous prices.

Read 7 tweets

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