Annalise  Hyatt

Annalise Hyatt


Time series forecasting with 2D convolutions

In this article, I will show you a time series forecasting method I haven’t seen documented elsewhere. I doubt it is a new method, but since I haven’t seen a great article on it, here it is.

The Dataset

The data I used for this project is the data from the Global Energy Forecasting competition, put on by my hometown university, UNC Charlotte. You can find more about it here:

What you need to know is the data is various readings from an energy grid. Our target is to forecast real-time energy demand for the grid using these data points. The data points also include dew point and dry bulb temperature, since air conditioning is a huge driver of energy consumption.

Our target variable is RTDemand: Real Time energy demand for the energy grid we are working with. The data has clear daily cycles. Here are three days of our data:

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Hourly for 3 days

In the middle of the night when the sun is down and everyone is asleep, our power consumption reaches a minimum. We wake up in the morning, head off to work, and our power consumption reaches its maximum as the sun reaches peak intensity. I think the daily dips correspond to commuting times.

If we zoom out a little more, we can see clear auto-correlation and trends in days, just as you see in weather. Here’s about 3 weeks of data:

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3 Weeks of the target variable

#energy #machine-learning #python #convolutional-network #time-series-analysis

What is GEEK

Buddha Community

Time series forecasting with 2D convolutions

What is Time Series Forecasting?

In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting.

What is Time Series Analysis?

In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.

Here some examples where time series is used.

  1. Business forecasting
  2. Understand the past behavior
  3. Plan future
  4. Evaluate current accomplishments.

Components of time series :

  1. Trend: Let’s understand by example, let’s say in a new construction area someone open hardware store now while construction is going on people will buy hardware. but after completing construction buyers of hardware will be reduced. So for some times selling goes high and then low its called uptrend and downtrend.
  2. **Seasonality: **Every year chocolate sell goes high during the end of the year due to Christmas. This same pattern happens every year while in the trend that is not the case. Seasonality is repeating same pattern at same intervals.
  3. Irregularity: It is also called noise. When something unusual happens that affects the regularity, for example, there is a natural disaster once in many years lets say it is flooded so people buying medicine more in that period. This what no one predicted and you don’t know how many numbers of sales going to happen.
  4. Cyclic: It is basically repeating up and down movements so this means it can go more than one year so it doesn’t have fix pattern and it can happen any time and it is much harder to predict.

Stationarity of a time series:

A series is said to be “strictly stationary” if the marginal distribution of Y at time t[p(Yt)] is the same as at any other point in time. This implies that the mean, variance, and covariance of the series Yt are time-invariant.

However, a series said to be “weakly stationary” or “covariance stationary” if mean and variance are constant and covariance of two-point Cov(Y1, Y1+k)=Cov(Y2, Y2+k)=const, which depends only on lag k but do not depend on time explicitly.

#machine-learning #time-series-model #machine-learning-ai #time-series-forecasting #time-series-analysis

Time Series Basics with Pandas

In my last post, I mentioned multiple selecting and filtering  in Pandas library. I will talk about time series basics with Pandas in this post. Time series data in different fields such as finance and economy is an important data structure. The measured or observed values over time are in a time series structure. Pandas is very useful for time series analysis. There are tools that we can easily analyze.

In this article, I will explain the following topics.

  • What is the time series?
  • What are time series data structures?
  • How to create a time series?
  • What are the important methods used in time series?

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on Medium 🌱 to see these posts and the latest posts.

Let’s get started.

#what-is-time-series #pandas #time-series-python #timeseries #time-series-data

Flow-Forecast: A time series forecasting library built in PyTorch

Flow Forecast is a recently created open-source framework that aims to make it easy to use state of the art machine learning models to forecast and/or classify complex temporal data. Additionally, flow-forecast natively integrates with Google Cloud Platform, Weights and Biases, Colaboratory, and other tools commonly used in industry.


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In some of my previous articles I talked about the need for accurate time series forecasts and the promise of using deep learning. Flow-Forecast was originally, created to forecast stream and river flows using variations of the transformer and baseline models. However, in the process of training the transformers I encountered several issues related to finding the right hyper-parameters and the right architecture. Therefore, it became necessary to develop a platform for trying out many configurations. Flow forecast is designed to allow you to very easily try out a number of different hyper-parameters and training options for your models. Changing a model is as simple as swapping out the model’s name in the configuration file.

Another problem I faced was how to integrate additional static datasets into the forecasts. For river flow forecasting, there was a lot of meta-data such as latitude, longitude, soil depth, elevation, slope, etc. For this, we decided to look into unsupervised methods like autoencoders for forming an embedding. This spurred the idea of creating a generic way to synthesize embedding with the temporal forecast.

Using flow forecast

There are a couple easy resources to use to get started with flow-forecast. I recorded a brief introduction video back in May and there are also more detailed live-coding sessions you can follow. We also have a basic tutorial notebook that you can use to get a sense of how flow-forecast works on a basic problem. Additionally, there are also a lot more detailed notebooks that we use for our core COVID-19 predictions. Finally, we also have ReadTheDocs available for in depth documentation as well as our official wiki pages.

#machine-learning #pytorch #time-series-analysis #time-series-forecasting #deep-learning #deep learning

Important for Time Series in Pandas

In the last post, I talked about working with time series . In this post, I will talk about important methods in time series. Time series analysis is very frequently used in finance studies. Pandas is a very important library for time series analysis studies.

In summary, I will explain the following topics in this lesson,

  • Resampling
  • Shifting
  • Moving Window Functions
  • Time zone

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on Medium 🌱 to see these posts and the latest posts.

Let’s get started.

#pandas-time-series #timeseries #time-series-python #time-series-analysis

Jamal  Lemke

Jamal Lemke


Hands-On Guide To Darts - A Python Tool For Time Series Forecasting

Data collected over a certain period of time is called Time-series data. These data points are usually collected at adjacent intervals and have some correlation with the target. There are certain datasets that contain columns with date, month or days that are important for making predictions like sales datasetsstock price prediction etc. But the problem here is how to use the time-series data and convert them into a format the machine can understand? Python made this process a lot simpler by introducing a package called Darts.

In this article, we will learn about Darts, implement this over a time-series dataset.

Introduction to Darts

For a number of datasets, forecasting the time-series columns plays an important role in the decision making process for the model. developed a library to make the forecasting of time-series easy called darts. The idea behind this was to make darts as simple to use as sklearn for time-series. Darts attempts to smooth the overall process of using time series in machine learning.

#developers corner #darts #machine learning #python #time series #time series forecasting