Alec  Nikolaus

Alec Nikolaus

1596450840

Time Series prediction using Adaptive filtering

Using adaptive filtering to predict the future time series value in Python

What is Adaptive filtering?

Adaptive filtering is a computational device that attempts to model the relationship between two signals, whose coefficients change with an objective to make the filter converge to an optimal state. The optimization criterion is a cost function, which is most commonly the mean square of the error between the output of the adaptive filter and the desired signal. The mean square error (MSE) will converge to its minimal value, while the filter adapts its coefficients. The figure below demonstrates the simple adaptive filter.

Image for post

Simple adaptive filter toolbox

The adaptive filter will try to match the filter output, y(k), with the desired signal, d(k). The adaptive filter will also learn using the error, e(k), and adjust the coefficient. Hence, it is adapted to the new environment, input x(k).

This brings us to the main feature of adaptive filtering, which is it has the **real-time capability to adjust the response with the intent to improve its performance **(sounds like self-learning, anyone?). The adaptation algorithm is implemented through two methods; gradient method and least square (LMS, RLS algorithm).

What is it for?

If you have studied any digital signal and processing courses, you will see most of the adaptive filter application on identifying an unknown communications channel, canceling noise or interference, or predicting the future values of a periodic signal.

So how we can use it in a business setting?

Based on the last example of the digital use case, we can apply the concept to predict future values in real-time settings, for example; stock price prediction. However, predicting the future using this approach requires several key assumptions; the data is either steady or slowly varying over time, and periodic overtime as well.

Accepting these assumptions, the adaptive filter must predict the future values of the desired output based on past input values. Hence, we need to structure the delay in the input signal and feed them to the adaptive filter system.

Image for post

Predicting future values using an adaptive filter

As stated earlier, the adaptive filter is used to identify and understand the unknown system, we can use this to identify and predict the time series behavior.

#python #data-science #machine-learning #time-series-analysis #adaptive-filtering

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Time Series prediction using Adaptive filtering
Alec  Nikolaus

Alec Nikolaus

1596450840

Time Series prediction using Adaptive filtering

Using adaptive filtering to predict the future time series value in Python

What is Adaptive filtering?

Adaptive filtering is a computational device that attempts to model the relationship between two signals, whose coefficients change with an objective to make the filter converge to an optimal state. The optimization criterion is a cost function, which is most commonly the mean square of the error between the output of the adaptive filter and the desired signal. The mean square error (MSE) will converge to its minimal value, while the filter adapts its coefficients. The figure below demonstrates the simple adaptive filter.

Image for post

Simple adaptive filter toolbox

The adaptive filter will try to match the filter output, y(k), with the desired signal, d(k). The adaptive filter will also learn using the error, e(k), and adjust the coefficient. Hence, it is adapted to the new environment, input x(k).

This brings us to the main feature of adaptive filtering, which is it has the **real-time capability to adjust the response with the intent to improve its performance **(sounds like self-learning, anyone?). The adaptation algorithm is implemented through two methods; gradient method and least square (LMS, RLS algorithm).

What is it for?

If you have studied any digital signal and processing courses, you will see most of the adaptive filter application on identifying an unknown communications channel, canceling noise or interference, or predicting the future values of a periodic signal.

So how we can use it in a business setting?

Based on the last example of the digital use case, we can apply the concept to predict future values in real-time settings, for example; stock price prediction. However, predicting the future using this approach requires several key assumptions; the data is either steady or slowly varying over time, and periodic overtime as well.

Accepting these assumptions, the adaptive filter must predict the future values of the desired output based on past input values. Hence, we need to structure the delay in the input signal and feed them to the adaptive filter system.

Image for post

Predicting future values using an adaptive filter

As stated earlier, the adaptive filter is used to identify and understand the unknown system, we can use this to identify and predict the time series behavior.

#python #data-science #machine-learning #time-series-analysis #adaptive-filtering

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 đźŚ± to see these posts and the latest posts.

Let’s get started.

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

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

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 đźŚ± to see these posts and the latest posts.

Let’s get started.

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

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas