Let's talk about nomenclature in Time Series Forecasting ✏️

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#MachineLearning #DataScience #Python
Time series data is a sequence of observations taken at specific time intervals. To understand it, we need to establish some key terms.
The current time is represented as t.

The observation made at the current time is represented as obs(t).
We often look at observations made at prior times, known as lag times or lags.

These are represented by negative numbers relative to the current time, e.g. t-1 for the previous time and t-2 for the time before that.
On the other hand, times in the future are represented by positive numbers relative to the current time, e.g. t+1 for the next time and t+2 for the time after that.

These are what we're interested in forecasting.
To make things simple, we often drop the obs(t) notation and use t+1 instead.

We also use shorthand such as a lag of 10 or lag=10 to refer to an observation at a lag.
📢 TL;DR

▶️ t-n represents a prior or lag time

▶️ t represents the current time and point of reference

▶️ t+n represents a future or forecast time
Understanding these standard terms is crucial for navigating time series data and making accurate predictions.
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More from @daansan_ml

Jan 16
What's the difference between Time Series Analysis and Forecasting? 🤔

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#machinelearning #python #datascience #ai
Time Series Analysis and Time Series Forecasting are both methods used to analyze and make predictions about data collected over time, but they have different focuses and applications.
▶️ Time Series Analysis is the process of understanding the underlying structure and patterns in a dataset.

It is used to identify trends, patterns, and relationships in the data, as well as to examine the behavior of a variable over time.
Read 8 tweets
Jan 14
Want to see the power of Time Series forecasting in action? 📈

Let's take a look at how the retail giant Walmart uses it to improve its operations. 🤯

🧵 THREAD🧵

#timeseries #forecasting #retail #example Image
Walmart has been using time series forecasting for decades to predict sales and optimise inventory levels.

By analysing historical sales data, weather patterns, and other factors, they're able to make more accurate predictions about future demand for products.
This allows them to ensure they have the right products in stock at the right time, reducing stockouts and increasing customer satisfaction.
Read 8 tweets
Jan 13
How can you transform your Time Series data to make it stationary? 🤔

Check below 7️⃣ ways of achieving this! 👇

🧵 THREAD🧵

#python #datascience #machinelearning #ai Image
1️⃣ Differencing:

This method involves subtracting the previous value from the current value.

This can help to remove any trend or seasonality in the data and make it stationary.
2️⃣ Moving Average:

This method involves taking the average of the time series over a certain period, such as a month or a year.

This can help to smooth out any fluctuations in the data and make it more stationary.
Read 10 tweets
Jan 6
Don't forecast future values in Time Series using the traditional way!! ⛔

Discover the Rolling forecasting! 🤯

#python #machinelearning #ml #ai #datascience
Yesterday we discussed the first way of forecasting with your Time Series model:
1️⃣ The traditional way or multi-step forecast

Today is time for the second (and better) way:

2️⃣ Rolling forecast
2️⃣ Rolling forecast

As mentioned yesterday, this consists of training the model every day, with all the available data until the present day.

Then we forecast for tomorrow. Image
Read 10 tweets
Jan 5
Be careful how you forecast the future in Time Series! 🤔

There are two main ways:

1️⃣ The traditional way or multi-step forecast

2️⃣ Rolling forecast

Let's see how they differ 👇

🧵 Thread🧵 Image
🔎 Let's consider the Apple stock price for this example.

⚠️ However, this is applicable to any Time Series data!
1️⃣ The traditional way or multi-step forecast

This consists of training the model once, with all the available data.

Then we forecast for several days in the future. Image
Read 10 tweets
Dec 13, 2022
Did you know about Facebook Prophet?

It's an open-source tool for forecasting Time Series data, and it's super easy to use!

Find out more about it!

🧵 👇

#DataScience #MachineLearning #Python #Facebook #AI
Facebook Prophet can handle missing data and changes in trends, which are common in time series data.
One of the best things about Facebook Prophet is its flexibility.

You can fine-tune your forecasts with a variety of parameters.
Read 7 tweets

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