##### よく話題になっている単語

動画数：22件

### Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka

🔥 Python Data Science Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") : https://www.edureka.co/data-science-python-certification-course
This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial:
1. Why Time Series?
2. What is Time Series?
3. Components of Time Series
4. When not to use Time Series
5. What is Stationarity?
6. ARIMA Model
7. Demo: Forecast Future
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#timeseries #timeseriespython #machinelearningalgorithms
- - - - - - - - - - - - - - - - -
About the Course
Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.
During our Python Certification Training, our instructors will help you to:
1. Master the basic and advanced concepts of Python
2. Gain insight into the 'Roles' played by a Machine Learning Engineer
3. Automate data analysis using python
4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
6. Explain Time Series and it’s related concepts
7. Perform Text Mining and Sentimental analysis
8. Gain expertise to handle business in future, living the present
9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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#yt:cc=on #Time Series Analysis #Time Series Analysis in Python #Time Series Forecasting #time series statistics #time series analysis and forecasting #python time series #machine learning for time series #machine learning algorithms #Time Series example #Time Series example in python #Time Series Analysis with python pandas #data science training #data science with python #python data science #edureka data science #edureka python #edureka machine learning #edureka

2018年05月31日

00:00:00 - 00:38:20

### Pythonで時系列データの未来予測をしてみよう〜SARIMAなど〜【時系列分析#3】

今回は「モデリング」と言われる技術を使って、時系列の未来予測をやっていきます。
この動画ではAR、MA、ARMA、ARIMA、SARIMA、SARIMAXといった時系列モデルを扱います。
※途中、ぐだぐだな部分がありますがご了承ください。。。
0:00 時系列予測の基礎知識
7:00 AR、MA、ARMA
12:24 ARMA以前のグリッドサーチ
14:33 ARIMA、SARIMA、SARIMAX
18:49 ARIMA以降のグリッドサーチ
29:04 ホワイトノイズの確認
33:38 未来予測
【キーワード】
AIC（赤池情報量規準）
#データサイエンス

2021年08月08日

00:00:00 - 00:37:08

### R Tutorial : Stationarity and Nonstationarity

Want to learn more? Take the full course at https://learn.datacamp.com/courses/arima-models-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Let's proceed with the basic concepts of stationarity, its importance, and how to coerce nonstationary data to stationarity.
In the context of time series, stationary refers to the stability of the mean - that is, there is no trend stability of the correlation - that is, the correlation structure of the data remains constant over time.
The time series plotted here may help in understanding stationarity better. The left-hand plot is stationary, there is no trend and the time series behaves the same, for example, between time points 1 to 50, 50 to 100, and so on. On the other hand, the plot on the right looks very different between time point 1 to 50 and 150 to 200. The means in these time intervals are different, as is the variability... the end of the series being more variable than the beginning.
Stationarity means that we can use simple averaging to estimate correlation: If the mean is constant, then you can estimate it by the sample average, x-bar, and If the correlation structure is constant, then for example, we can use all the pairs of data that are 1 time unit apart, (x_1, x_2), (x_2, x_3), and so on, to estimate lag 1 correlation. This works because the relationship between contiguous values of the series remains the same over time. Similarly, we can use (x_1, x_3), (x_2, x_4) and so on to estimate the lag 2 correlation.
The Southern Oscillation Index is reasonably stable.... it looks the same in any little segment of time (although there might be some slight trend).
The scatterplots show the correlation in terms of lag. This is called auto-correlation and is the same as the correlation you learned about in regression. The graphic shows that the Southern Oscillation Index, which is a surrogate for sea surface temperature, is positively correlated with itself one month apart, but negatively correlated with itself six months apart (as it is hot in the summer and cold in the winter).
The global temperature deviations are an example of a random walk where the value of the series at time t is the value it was at time t-1 plus a completely random movement. Differencing ("today minus yesterday") can make this kind of process stationery.
The price of chicken is more like "trend stationarity", which is stationary behavior around a simple trend. Differencing works here too.
Finally, if there are trend and heteroscedasticity, logging and differencing can help as in the Johnson and Johnson earnings data set. First, logging positive-valued data can stabilize the variance. Second, differencing the data will detrend it.
#DataCamp #RTutorial #ARIMAModelsinR
#ARIMA Models in R #DataCamp #R Tutorial #want to learn R #Data Science #how to learn data science #Time Series with R #Quantitative Analyst with R #R stats package #how to fit various ARMA models #R package astsa #how to fit integrated ARMA models #ARIMA models #how to check the validity of an ARIMA model #how to forecast time series data #learn the basics of ARMA models #basic R commands #Stationarity and Nonstationarity

2020年03月25日

00:00:00 - 00:03:14

### R Tutorial : Welcome to Forecasting Using R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/forecasting-using-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Hi. I'm Rob Hyndman. I'm a Professor of Statistics at Monash University in Australia. I'll be your instructor for this DataCamp course on Forecasting In R.
In this course, we will learn how to visualize time series in order to discover the useful information we need for forecasting. We will also consider some very simple forecasting methods, some intermediate level methods such as exponential smoothing and ARIMA models, as well as some more advanced methods. Throughout the course, we will measure how accurate our forecasts are, and how to decide which method to use in each case.
Everything we cover in this course is discussed in more detail in my textbook with George Athanasopoulos. It is freely available online, so you can always refer to it if you want more information. The book uses R throughout and shows the code for almost all graphs and analyses.
Prediction is a big topic, and in this course, we are going to focus on a particular type of prediction, namely forecasting time series. A time series is simply a series of data observed over time. In this course, we deal only with regularly spaced time series. For example, the data could be observed every hour, every day, every month, every quarter, or every year. Provided the observation intervals are equally spaced, we call them a regularly spaced time series.
Here is an example of a monthly time series of total expenditure on eating out in Australia. There is a strong trend, driven by a mix of population growth and an increase in disposable income, and there is some seasonality. In recent years, eating-out costs have peaked in December (due to Christmas and end-of-year events) and drop in February (due to it being a short month).
Forecasting is the task of estimating how a time series like this will continue into the future. Of course there is considerable uncertainty associated with such estimates, so we normally also provide an estimate of that uncertainty in the form of prediction intervals.
This is an example of what such forecasts might look like. In this example, the forecast values are shown in blue, along with 80% prediction intervals and 95% prediction intervals. These indicate how uncertain our forecasts are. The further ahead we forecast, the wider these prediction intervals tend to be.
In this course, we will discuss various forecasting methods for time series data that take account of trend, seasonality and other features of the data. We will also look at how we can measure the accuracy of these forecasts, and how to go about selecting a good forecasting model.
That's enough background information. Let's get going with your first interactive exercise.
#DataCamp #RTutorial #ForecastinginR
#Forecasting in R #Quantitative Analyst with R #Time Series with R #time series forecasting using R #DataCamp #R Tutorial #want to learn R #Data Science #how to learn data science

2020年03月14日

00:00:00 - 00:02:39

### Time Series Analysis and Forecasting: An Overview for Beginner Data Scientists

An overview of time series analysis and forecasting. This talk is meant for individuals who are beginner data scientists with basic coding experience. Time series data problems are quite common and in demand. Time series analysis and models are used in areas such as finance, retail, human resources, environmental analysis, and more. The audience will be able to navigate, analyze, and model time series data, and apply these techniques to the problems of their choice.
This webinar is meant for individuals who are beginner data scientists with basic coding experience. By the end of this webinar individuals will be able to:
- Understand the basics of time series analysis and the related terminology
- Use machine learning to create time series models
- Forecast with, and validate these models
- Make interactive dashboards
There will also be an interactive exercise: A skeleton notebook and dashboard script will be provided. The data will be a financial set about stock prices.
0:00 Intro
2:03 Cross Sectional VS. Time Series
4:14 Why is Time Series Important
7:10 Creating Your Time Series Problem
10:52 Time Series Components
16:11 Decomposition Model
24:33 Autoregression
25:43 Moving Average
29:40 Stationarity and Augmented Dickey-Fuller Test
31:45 Integration - ARIMA Model
42:08 Residual Analysis
43:54 Ljung-Box Test
45:01 Aditional Questions
51:20 Autocorrelation Function
54:16 Interpretating ACF and PACF Plots
55:50 Interpreting Seasonal Orders
59:35 Conclusion
1:00:57 Q&A
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#timeseriesanalysis #timeseries #datascience
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2023年03月23日

00:00:00 - 01:08:47

### Python Tutorial: Intro to AR, MA and ARMA models

Want to learn more? Take the full course at https://campus.datacamp.com/courses/arima-models-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Now you know how to prepare your data, lets dive straight into the models. We will discuss AR and MA models and how these are combined into ARMA models.
In an autoregressive model we regress the values of the time series against previous values of this same time series.
The equation for a simple AR model is shown here. The value of the time series at time t, is a-one times the value of the time series at the previous step. There's also a shock term, epsilon-t. The shock term is white noise, meaning each shock is random and not related to the other shocks in the series. a-one is the autoregressive coefficient at lag one.
Compare this to a simple linear regression where the dependent variable is y-t and the independent variable is y-t-minus-one. The coefficient a-one is just the slope of the line and the shocks are the residuals to the line.
This is a first order AR model. The order of the model is the number of time lags used. An order two AR model has two autoregressive coefficients, and has two independent variables, the series at lag one and the series at lag two.
More generally, we use p to mean the order of the AR model. This means we have p autoregressive coefficients and use p lags.
In a moving average model we regress the values of the time series against the previous shock values of this same time series.
The equation for a simple MA model is shown here. The value of the time series, is m-one times the value of the shock at the previous step; plus a shock term for the current time step.
This is a first order MA model. Again, the order of the model means how many time lags we use. An MA two model would include shocks from one and two steps ago.
More generally, we use q to mean the order of the MA model.
An ARMA model is a combination of the AR and MA models.
The time series is regressed on the previous values and the previous shock terms. This is an ARMA-one-one model.
More generally we use ARMA-p-q to define an ARMA model. The p tells us the order of the autoregressive part of the model and the q tells us the order of the moving-average part.
Using the statsmodels package, we can both fit ARMA models and create ARMA data. Lets take this ARMA-one-one model. Say we want to simulate data with these coefficients.
First we import the arma-generate-sample function.
Then we make lists for the AR and MA coefficients. Note that both coefficient lists start with one. This is for the zero lag term and we will always set this to one. We set the lag one AR coefficient as 0.5 and MA coefficient as 0.2.
We generate the data, passing in the coefficients, the number of data points to create, and the standard deviation of the shocks.
Here, we actually pass in the negative of the AR coefficients we desire. This is a quirk we will need to remember. Our sample may look like this.
Fitting is covered in the next chapter, but here is a quick peek at how we might fit this data.
First we import the ARMA model class.
We instantiate the model, feed it the data and define the model order.
Then finally we fit. You've learned a lot. Now let's practice!
---
#PythonTutorial #Python #DataCamp #timeseries #stationarity #ARIMA #AR #MA
#timeseries #DataCamp #Python #PythonTutorial #stationarity #ARIMA #AR #MA

2020年03月11日

00:00:00 - 00:04:10

### R Tutorial : ARIMA Models in R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/arima-models-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Hi, and welcome to DataCamp's course on ARIMA modeling with R!
My name is David Stoffer. I am Professor of Statistics at the University of Pittsburgh. I am the coauthor of two texts on time series analysis. One of them, "Time Series Analysis and Its Applications: With R Examples" is the basis of this course. The text has a companion R package called astsa which stands for Applied Statistical Time Series Analysis. This package will be used throughout the course.
Now, to get started, let's explore the nature of time-series data.
Here we have the Johnson & Johnson quarterly earnings per share series. It has some common features of time series data, upward trend, seasonality in that the 2nd and 3rd quarters usually up, while the 4th quarter is usually down. In addition, there is heteroscedasticity because, as the value of the asset grows, small percent changes become large absolute changes.
The second series is the annual global temperature deviations. The data are deviations from the average temperature between 1960 and 1980. You will notice that the data have a generally positive trend, but the trend is not always positive. Unlike the Johnson and Johnson data, this series does not have a seasonal component and it is homoscedastic.
The third series is the S&P 500 weekly returns. The S&P 500 is a US stock index based on 500 large corporations. Returns are the percent change per time period. Unlike the other series, this series does not have any trend or seasonality. In fact, it seems like there are not any patterns in the series (except that once in a while, the variance is big). This is an example of a particular kind of process called noise.
Next, we will describe some models that can be used to analyze the types of time series data we have seen.
ARMA models are time series regression models. If you recall, in regression you have a dependent variable (Y), an independent variable (X), and you linearly regress Y on X. A crucial assumption is that the errors are independent, normal, and homoscedastic. In other words, the errors are white noise.
White noise is a sequence of independent normals with common variance. You will eventually see that time series models are built around white noise.
With time series, you can regress today on yesterday, and this is called an auto (or self) regression. In this case, what happens today is the dependent variable and what happened yesterday is the independent variable. As it is written, the errors are white noise.
Typically, time series data are correlated, and assuming the errors are not correlated may lead to bad forecasts. One way to overcome the problem is to use a moving average for the errors. In this example, note that the error at time t is correlated with the error at time t-1 because they both have a W_{t-1}.
Putting the two together leads to the ARMA model. In other words, the model is auto-regression with autocorrelated errors.
#DataCamp #RTutorial #ARIMAModelsinR
#ARIMA Models in R #DataCamp #R Tutorial #want to learn R #Data Science #how to learn data science #Time Series with R #Quantitative Analyst with R #R stats package #how to fit various ARMA models #R package astsa #how to fit integrated ARMA models #ARIMA models #how to check the validity of an ARIMA model #how to forecast time series data #learn the basics of ARMA models #basic R commands

2020年03月25日

00:00:00 - 00:03:35

### ARIMA modeling and forecasting | Time Series in Python Part 2

In part 2 of this video series, learn how to build an ARIMA time series model using Python's statsmodels package and predict or forecast N timestamps ahead into the future. Now that we have differenced our data to make it more stationary, we need to determine the Autoregressive (AR) and Moving Average (MA) terms in our model. To determine this, we look at the Autocorrelation Function plot and Partial Autocorrelation Function plot.
0:00 – Introduction
1:12 – ACF and PACAF plots
2:50 – Building the ARIMA model
5:53 – Forecasting
8:27 – Comparison
Watch Part 1 Here:
https://tutorials.datasciencedojo.com/time-series-python-reading-data/
Watch Part 3 Here:
https://tutorials.datasciencedojo.com/mean-absolute-error-forecast/
Code, R & Python Script Repository:
https://code.datasciencedojo.com/rebeccam/tutorials/tree/master/Time%20Series
Packages Used:
pandas
matplotlib
StatsModels
statistics
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2019年05月02日

00:00:00 - 00:12:32

### Read and Index your data with pandas | Time Series in Python Part 1

In part 1 of this video series, learn how to read and index your data for time series using Python’s pandas package. We check if the data meets the requirements or assumptions for time series modeling by plotting to see if it follows a stationary pattern. We also transform our data by taking differences in the values to make them more stationary.
Table of Contents:
0:00 – Introduction
1:21 – Read and index
6:12 – Plot the data
Part 2: https://tutorials.datasciencedojo.com/arima-model-time-series-python/
Part 3: https://tutorials.datasciencedojo.com/mean-absolute-error-forecast/
Dataset, Python and R Scripts:
https://code.datasciencedojo.com/rebeccam/tutorials/tree/master/Time%20Series
Packages Used:
pandas
matplotlib
StatsModels
statistics
Get started with Python for data science:
https://tutorials.datasciencedojo.com/getting-started-with-python-and-r/
Check out web scraping in R!
https://tutorials.datasciencedojo.com/r-tutorial-web-scraping-rvest/
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2019年04月25日

00:00:00 - 00:10:46

### Time Series overview with the KNIME Analytics Platform

What is time series? Why time series? In this session, you’ll learn about the main concepts behind Time Series: preprocessing, alignment, missing value imputation, forecasting, and evaluation. Together we will build a demand prediction application: first with (S)ARIMA models and then with machine learning models. The codeless examples are built in the KNIME Analytics Platform using the Time Series components provided for preprocessing, transforming, aggregating, forecasting, and inspecting time series data. You will also be provided example workflows to use later in your own projects.
--
For more captivating community talks featuring renowned speakers, check out this playlist: https://youtube.com/playlist?list=PL8eNk_zTBST-EBv2LDSW9Wx_V4Gy5OPFT
To gain a better understanding of what data scientists do and how they work, check out this playlist: https://youtube.com/playlist?list=PL8eNk_zTBST9zccqrEhDDkjMZK1k3Aagl
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At Data Science Dojo, we believe data science is for everyone. Our data science trainings have been attended by more than 10,000 employees from over 2,500 companies globally, including many leaders in tech like Microsoft, Google, and Facebook. For more information please visit: https://hubs.la/Q01Z-13k0
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2022年05月20日

00:00:00 - 01:00:18

### R Tutorial: Forecasting with time series

Want to learn more? Take the full course at https://learn.datacamp.com/courses/forecasting-product-demand-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Now that we have built the model, we need to forecast the future values of our data! That's why we are here!
The best part about time series models using information from the past is that you could forecast the future. If we know the pattern on how an observation at one time period is related to an observation at another time period, then we can recursively forecast the future using this pattern (assuming it stays the same).
What time-series models are doing is essentially finding the pattern - or signal - in your data. They are then extrapolating this signal repeatedly over time. Now extrapolation sounds really bad, so instead, we call it forecasting. Of course, this forecast is not going to be perfect. In your data, you have both signal and noise. The remaining noise is what helps us estimate confidence intervals around our forecasts.
The forecast function in R makes forecasting time series models easy to do. You need to put in your model object as well as how many time periods into the future you would like to forecast - that is the "h = " option. As you can see here, we want to forecast our time series model 22 observations into the future.
Let's visualize this forecast. You could just use the plot function, but I want to compare the forecast with the validation set. First, create an xts object from the forecast, called the "mean" attribute of your forecast object, then the plot function for your validation and the lines for your forecast.
Hmmm... That's an interesting looking forecast.
There are a lot of ways of measuring accuracy in time series models. Two of the most common ways of doing so are the Mean Absolute Error, called the MAE, and the Mean Absolute Percentage Error called the MAPE.
The MAE is the average measure of how far away, in absolute terms, your prediction is from the actual value. The best part of the MAE is that it is easily measured in the scale of your data. However, that is also the downside. How so? What if I told you my prediction was off on average by $100,000? If I was predicting US GDP, then you would be very impressed! If I was predicting the price of my hamburger dinner last night, then you would think I have no clue what I am doing! Without a reference, an average prediction error might be out of place.
That is what the MAPE is for! The MAPE is the average measure of how far away in absolute PERCENTAGE terms, your prediction is from the actual value. This makes your prediction not dependent on scale.
The forecast object that we created for the mountain region, called forecast_M_t, has many components, not just the forecast. To get the forecast, we need to ask for the mean object with the $.
To make it easier to compare the forecast with the validation data set we also use the as dot numeric functions. Once we do that we can easily calculate both the MAE and MAPE.
Let's see how we did! Well, for the MAE we were only off by around 199 products sold on average in the mountain region! Is that good? Not sure. Let's check the MAPE. It looks like we were off by a little more than 9.5% on average. That might put things in better context.
Now let's see how well you can predict the metropolitan region!
#R Tutorial #data science #datacamp #data analysis #Time Series Data in R #Forecasting Product Demand in R #Loading data into xts object #ARIMA models #forecasting in r #arima in r #Forecasting with time series

2020年02月26日

00:00:00 - 00:04:36

### R Tutorial : Stationary Time Series: ARMA

Want to learn more? Take the full course at https://learn.datacamp.com/courses/arima-models-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
You are probably wondering why it is valid to use ARMA models for stationary time series data. This question was answered in part by Hermann Wold, who showed that any stationary time series can be written as a linear combination of white noise.
We can also show that an any ARMA model is a linear combination of white noise. This means that ARMA models are well suited for describing the dynamics of stationary time series.
The moving average model is already in this form. As it turns out, all ARMA models are of this form.
R provides an easy way to simulate these models. It is called 'arima.sim'. The basic syntax are to specify the model using a list, and then specify how many observations you want. There are a few ways to specify the model, but the easiest way is to specify the order, p- the AR order, d which we haven't discussed yet, and q- the MA order.
For example, to generate data from an MA(1) with parameter .9, specify the model as a list with order=c(0,0,1) and ma=.9. In this case, we'll generate 100 observations.
In this example we'll generate and plot 100 observations from an AR(2) with parameters 0 and -.9. Note that the data are somewhat cyclic, like the southern oscillation index.
Ok, your turn!
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2020年03月25日

00:00:00 - 00:01:44

### Evaluating Our Model with statistics and matplotlib | Time Series in Python Part 3

In part 3 of this video series, learn how to evaluate time series model predictions using Python's statistics and matplotlib packages. We look at plotting the differences between actual versus predicted values, and calculate the mean absolute error to help evaluate our ARIMA time series model. We also look at potential issues when modeling time series, and how to take this further and learn more in-depth.
0:00 – Introduction
0:45 – Reading the sample
4:19 – Predicted values to the training set
5:15 – Plot actual versus predicted values
7:05 – Evaluating the model
12:22 – Testing the model
Watch Part 1:
https://tutorials.datasciencedojo.com/arima-model-time-series-python/
Watch Part 2:
https://tutorials.datasciencedojo.com/arima-model-time-series-python/
Code, R & Python Script Repository:
https://code.datasciencedojo.com/rebeccam/tutorials/tree/master/Time%20Series
Packages Used:
pandas
matplotlib
StatsModels
statistics
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2019年05月09日

00:00:00 - 00:16:17

### Python Tutorial: Making time series stationary

Want to learn more? Take the full course at https://campus.datacamp.com/courses/arima-models-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Last time we learned about ways in which a time series can be non-stationary, and how we can identify it by plotting.
However, there are more formal ways of accomplishing this task, with statistical tests.
There are also ways to transform non-stationary time series into stationary ones.
We'll address both of these in this lesson and then you'll be ready to start modeling.
The most common test for identifying whether a time series is non-stationary is the augmented Dicky-Fuller test.
This is a statistical test, where the null hypothesis is that your time series is non-stationary due to trend.
We can implement the augmented Dicky-Fuller test using statsmodels. First we import the adfuller function as shown, then we can run it on our time series.
The results object is a tuple. The zeroth element is the test statistic, in this case it is -1.34.
The more negative this number is, the more likely that the data is stationary.
The next item in the results tuple, is the test p-value. Here it's 0.6. If the p-value is smaller than 0.05, we reject the null hypothesis and assume our time series must be stationary.
The last item in the tuple is a dictionary. This stores the critical values of the test statistic which equate to different p-values. In this case, if we wanted a p-value of 0.05 or below, our test statistic needed to be below -2.91.
We will ignore the rest of the tuple items for now but you can find out more about them here.
Remember that it is always worth plotting your time series as well as doing the statistical tests. These tests are very useful but sometimes they don't capture the full picture.
Remember that Dicky-Fuller only tests for trend stationarity.
In this example, although the time series behavior clearly changes, and is non-stationary, it passes the Dicky-Fuller test. So let's say we have a time series that is non-stationary. We need to transform the data into a stationary form before we can model it. You can think of this a bit like feature engineering in classic machine learning.
Let's start with a non-stationary dataset. Here is an example of the population of a city. One very common way to make a time series stationary is to take its difference.
This is where, from each value in our time series we subtract the previous value. We can do this using the dot-diff method of a pandas DataFrame. Notice that this gives us one NaN value at the start since there is no previous value to subtract from it. We can get rid of this using the dot-dropna method.
Here is the time series after differencing. This time, taking the difference was enough to make it stationary, but for other time series we may need to take the difference more than once.
Sometimes we will need to perform other transformations to make the time series stationary.
This could be to take the log, or the square root of a time series, or to calculate the proportional change.
It can be hard to decide which of these to do, but often the simplest solution is the best one.
You've learned how to test for stationarity and make time series stationary. Now let's practice!
#PythonTutorial #Python #DataCamp #timeseries #stationarity #ARIMA
#DataCamp #Python #PythonTutorial #timeseries #stationarity #ARIMA

2020年03月11日

00:00:00 - 00:03:52

### DataChats | Episode 12 | An Interview With David Stoffer

In this episode of DataChats Lore talks with David Stoffer. Interested in learning more? Start David's ARIMA Modeling with R course today: https://www.datacamp.com/courses/arima-modeling-with-r
David Stoffer is a Professor of Statistics at the University of Pittsburgh. He is member of the editorial board of the Journal of Time Series Analysis and Journal of Forecasting. David is the coauthor of the book "Time Series Analysis and Its Applications: With R Examples", which is the basis of his course. Another (free) book he wrote on Time Series Analysis is available here: http://www.stat.pitt.edu/stoffer/tsa4/tsaEZ.pdf
Together with Lore, David talks about his path to Statistics, his teaching method, his latest book, how he got into R, and much more.
#rstats #r programming #data science #data analysis #learn r #r tutorial #big data #data #r for data science #r for data analysis #David Stoffer #Time series analysis #Time series with R #Time series #ARIMA #ARIMA tutorial #ARIMA tutorial in R #datacamp #learn Data Science #arima modeling

2017年03月04日

00:00:00 - 00:08:01

### Python Tutorial: Introduction to time series and stationarity

Want to learn more? Take the full course at https://learn.datacamp.com/courses/arima-models-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Welcome to this course on forecasting using ARIMA models in Python. My name is James Fulton and I will be your guide as you learn how to predict the future of time series.
Time series data is everywhere in this world. It is used in a wide variety of fields. There are many datasets for which we would like to be able to predict the future. Knowing the future of obesity rates could help us intervene now for public health; predicting consumer energy demands could help power stations run more efficiently; and predicting how the population of a city will change could help us build the infrastructure we will need.
We can forecast all of these datasets using time series models, and ARIMA models are one of the go-to time series tools.
You will learn how to fit these models and how to optimize them.
You will learn how to make forecasts of important real-world data, and importantly how to find the limits of your forecasts.
Let's start by examining a time series. We can load a time series from csv using pandas. Here we set the index as the date column and parse the date into datetime data-type.
To plot the data we make a pyplot figure and use the DataFrame's dot-plot method.
One important feature of a time series is its trend. A positive trend is a line that generally slopes up - the values increase with time. Similarly, a negative trend is where the values decrease.
Another important feature is seasonality. A seasonal time series has patterns that repeat at regular intervals, for example high sales every weekend.In contrast, cyclicality is where there is a repeating pattern but no fixed period.
White noise is an important concept in time series and ARIMA models. White noise is a series of measurements, where each value is uncorrelated with previous values.
You can think of this like flipping a coin, the outcome of a coin flip doesn't rely on the outcomes of coin flips that came before. Similarly, with white noise, the series value doesn't depend on the values that came before.
To model a time series, it must be stationary. Stationary means that the distribution of the data doesn't change with time. For a time series to be stationary it must fulfill three criteria. These are:
The series has zero trend, it isn't growing or shrinking.
The variance is constant. The average distance of the data points from the zero line isn't changing And the autocorrelation is constant. How each value in the time series is related to its neighbors stays the same.
Generally, in machine learning, you have a training set which you fit your model on, and a test set, which you will test your predictions against. Time series forecasting is just the same.
Our train-test split will be different however. We use the past values to make future predictions, and so we will need to split the data in time. We train on the data earlier in the time series and test on the data that comes later.
We can split time series at a given date as shown above using the DataFrame's dot-loc method.
We've learned the basics of stationarity and train-test splitting. Let's get used to these in practice.
#PythonTutorial #Python #DataCamp #timeseries #stationarity #ARIMA
#DataCamp #Python #PythonTutorial #timeseries #stationarity #ARIMA

2020年03月11日

00:00:00 - 00:03:45

### Time Series Analysis using Python | Time Series Forecasting | Data Science with Python | Edureka

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This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial:
1. Why Time Series?
2. What is Time Series?
3. Components of Time Series
4. When not to use Time Series
5. What is Stationarity?
6. ARIMA Model
7. Demo: Forecast Future
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2023年05月18日

00:00:00 - 00:29:02

### R Tutorial: ARIMA Time Series 101

Want to learn more? Take the full course at https://learn.datacamp.com/courses/forecasting-product-demand-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
If we are going to forecast our sales with time series modeling, we need to have a quick look at one of the foundational models of time series - ARIMA models.
Now you could spend an entire course on this.
In fact, Datacamp has some really good courses on time series. If you are really interested in more details about these models I would highly recommend these courses. For now, I will give you a brief overview of ARIMA modeling in this video.
Let's quickly break down what we mean when we say ARIMA modeling.
AR stands for autoregressive. MA stands for moving average. I stands for integrated. Let's actually start with the integrated piece first.
In time series models we typically assume dependency across time. Otherwise, why would we care about time at all? The big questions are if this dependency exists and how long does it stays.
A rather crude definition of stationarity is when effects in a data set dissipate as time goes on. What happens today has less and less effect on the data the further we get from today.
The best long-term predictions for data that has stationarity is the historical mean of the series. A historical average wouldn't be a good prediction for something with a wave or something always trending up.
How do we make our data have stationarity? Typically, through differencing.
Differencing your data means looking at the change from one time period to another or the DIFFERENCE between them.
This can solve trending data sets with a single time period difference. It can also solve seasonal effects with seasonal differences. For example, monthly sales data might have an annual (or 12 period) seasonal wave.
Once you have stationary data you can move to the other pieces of the ARIMA model.
Autoregressive models deal with previous values of your data. For example, last month's sales have some residual effect on what sales look like this month. These previous values are called lags and you can have any number of them in your model. They are called long-memory models because these effects slowly dissipate across time.
Moving average models deal with previous, hidden "shocks" or errors in your model. This is harder to conceptualize. Essentially, how "abnormal" your previous value was compared to what was predicted last month has some residual effect on this month's sales. They are called short-memory models because these effects quickly disappear completely.
Before we model we still need to split our data into training and validation. It is always good practice to compare our predictions against real data to see how good our model REALLY is.
We are going to combine both products in the mountain region - high end and low end - into total mountain sales. Next, we are just splitting off the 2017 data for our sales in the mountain region for validation.
Once we have our training data, R has a wonderful function called auto.arima(). This function will try to estimate the ARIMA model you need for your data. As you can see here, the function tells us all the pieces of the ARIMA model. It says that the AR piece (the first piece) has four previous values of Y that should be in the model. It then says that there is no differencing necessary for the data - the second piece is zero. Finally, there is one moving average piece to our model as well. If you are really interested, the coefficients for these are listed as well.
Wow! That was a lot to learn about time series really quickly. Let's solidify those concepts.
#R Tutorial #data science #datacamp #data analysis #Time Series Data in R #Forecasting Product Demand in R #Loading data into xts object #ARIMA models #forecasting in r #arima in r #ARIMA Time Series 101

2020年02月26日

00:00:00 - 00:04:53

### Python Tutorial : Three flavors of Machine Learning

Want to learn more? Take the full course at https://learn.datacamp.com/courses/fundamentals-of-ai at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
To better understand Machine Learning, let's investigate its three most common flavors: Supervised, Unsupervised, and Reinforcement learning.
Supervised learning is the most common flavor of machine learning in use today.
Companies use it to predict employee performance, what product you're likely to buy next, are you likely to repay the loan you are applying for and much more.
We use it to build models that predict categories or quantities based on some input measurements. So, if we are making a Fruit and Vegetable recognizer, the training inputs will be pictures and training outputs the labels stating which fruit or veggie is in the picture.
The usage of output labels during training is where the name "supervised" comes from.
There are two major problem types in supervised learning: Regression problems, when the output of interest is a quantity -- such as length, weight or oil prices; and Classification problems, where we want to predict categories, such as "metal or plastic", "positive or negative review".
Most common models for tackling regression problems are Linear regression, Lasso and Ridge regression, as well as ARIMA models which are used for time-series forecasting.
For classification, most common models are Logistic regression, Bayesian classifiers and Tree-based models (such as Decision Trees, Random Forests and Gradient Boosted Trees).
As for neural networks, they are so versatile that, in the right configuration, they can be used to tackle both problems.
Unsupervised learning owes its name to the fact that at training time it makes no use of the output labels -- it is only busy with capturing the relationships and patterns in process inputs.
One typical problem we can solve in this way is finding groups of similar entities or events -- for example, groups of similar consumers of a certain product, or similar articles on a news website.
We call this problem "clustering" and it is crucial to differentiate it from its supervised sibling Classification.
With classification, we are teaching the model some pre-existing categorizations, while with clustering we are exploring and discovering categories, with minimum assumptions.
Another important problem solved by unsupervised learning is Anomaly detection -- used to detect abnormal entities and events, like the ones in the ECG signal shown on the picture.
And lastly, there is Dimensionality Reduction -- used to reduce complex, high-dimensional datasets to a simplified representation. We might do this to minimize overfitting, or to reduce the computational intensity or just to be able to visualize complex data in 2D.
When it comes to algorithms, the most famous Clustering algorithm is K-means clustering, but a variety of them exists, like mean-shift clustering, DBSCAN and others.
For Dimensionality reduction, the first choice is usually Principal Component Analysis or PCA, followed by an array of non-linear algorithms, also called "Manifold learning".
Finally, for Anomaly detection, an excellent first choice is the Isolation Forest algorithm.
Last but not least, we have a very interesting domain of Reinforcement Learning, which is not covered in this course, but absolutely necessary to mention.
Reinforcement learning is most similar to the natural way in which living organisms learn: an entity or an "agent" is taking certain actions in its environment and then adjusting its behavior depending on whether the outcome of the action was positive or negative compared to its success criteria.
Although a very powerful idea and easy to intuitively understand, this domain of AI is still in its infancy, but significant efforts are being invested in research within this domain.
Ok, you got it! We made a quick flyover across the vast AI landscape -- let's practice the learnings from this chapter and then we'll take deeper dives into Supervised, Unsupervised and Deep Learning.
#DataCamp #PythonTutorial #AIFundamentals
#Three flavors of Machine Learning #AI Fundamentals #Data Skills for Business #Machine Learning Scientist with Python #how to define constants and variables #DataCamp #Python Tutorial #want to learn Python #Data Science #how to learn data science #Data Analyst with Python #Data Scientist with Python #Machine Learning #Deep Learning #Predictive Analytics #All models are wrong but some are useful #Supervised #Unsupervised #Reinforcement learning

2020年04月19日

00:00:00 - 00:04:42

### 【説明可能なAI】データのミスラベルを発見？! Neural Network Consoleでの実装を解説

この動画は，ニューラルネットワークの訓練時にデータが与えた影響度を算出することで，訓練データに混在するラベルミスデータを検出する手法であるTracInのNeural Network Console上での実装方法に関する解説動画です。
■Neural Network Consoleのダウンロード(Windows版)
https://dl.sony.com/ja/app/
■ダウンロードリンク(github)
動画内で触れられているコンテンツのリンクです。
https://github.com/sony/nnc-plugin (nnc-plugin)
■論文リンク
https://proceedings.neurips.cc/paper/2020/hash/e6385d39ec9394f2f3a354d9d2b88eec-Abstract.html
Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan,
“Estimating Training Data Influence by Tracing Gradient Descent”, NeurIPS 2020.
■Neural Network Libraries での実装
https://github.com/sony/nnabla-examples/tree/master/responsible_ai/tracin
■動画チャンネル紹介
ソニーが提供するオープンソースのディープラーニング（深層学習）フレームワークソフトウェアのNeural Network Libraries（ https://nnabla.org/, https://github.com/sony/nnabla/ ）に関連する情報を紹介する動画チャンネルを開設しました（ https://www.youtube.com/c/nnabla ）。Neural Network Librariesのチュートリアル・Tipsに加え、最先端のディープラーニングの技術情報（講義、最先端論文紹介）などを発信していきます。チャンネル登録と応援よろしくおねがいします！
同じくソニーが提供する直感的なGUIベースの深層学習開発環境のNeural Network Console（ https://dl.sony.com/ ）が発信する大人気のYouTubeチャンネルでもディープラーニングの技術講座やツールのチュートリアルを多数公開しています。こちらもチャンネル登録と応援よろしくおねがいします(https://www.youtube.com/c/NeuralNetworkConsole)。

2022年01月11日

00:00:00 - 00:12:40

### R Tutorial: Loading data into xts object

Want to learn more? Take the full course at https://learn.datacamp.com/courses/forecasting-product-demand-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
Hello! I'm Aric LaBarr and I will be your instructor for this course on forecasting product demand.
Before we can do anything fancy with modeling, we need to understand how to bring our data into R and make it ready for time series analysis.
To do this we are going to use xts objects!
To make the data easier to forecast, we will be making an xts object out of the data. An xts object stands for extensible time series. It essentially builds upon zoo objects which are commonly used for time structured data in R. The easiest way I like to think about xts objects are essentially a data matrix that is indexed across time. This indexing makes exploration and manipulation of the data extremely easy.
If you are interested in learning more granular details about xts objects,
I highly recommend taking these DataCamp courses.
Remember that an xts object contains two pieces - the date index and the data matrix. The data has already been loaded into a matrix for you, but we need to create a date index. Luckily it is rather easy to do just that with the `seq` function in R. Just specify a starting point, which is typically easier to do with an as dot Date function, a length of the vector of dates, and what unit of time you would like the vector to be in. Here we want 176 weeks starting on January 19, 2014.
Creating an xts object from there is extremely easy using the `xts` function! Just specify your data matrix - here called bev - and your date index with the `order dot by` option.
Now each of your observations is indexed by a date as you can see here for our mountain high end product called 'M dot hi' in our data set. One nuance you can see here about `xts` objects are that you can call columns by name inside of `xts` objects. For example, your `xts` object called `bev_xts` with a column named `M dot hi` you could call that column specifically as you see here in the slides.
Now you try manipulating data with the xts function!.
#R Tutorial #data science #datacamp #data analysis #Time Series Data in R #Forecasting Product Demand in R #Loading data into xts object #ARIMA models #forecasting in r #arima in r

2020年02月26日

00:00:00 - 00:02:52

### Advanced Predictive Modelling in R | Predictive Modelling Techniques | What is Predictive Modelling

Watch Sample Recording : http://www.edureka.co/about-advanced-predictive-modelling-in-r?utm_source=youtube&utm_medium=referral&utm_campaign=apmr-what-is-pred-mod
Predictive modelling leverages statistics to predict outcomes.[1] Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place.
Edureka's Advanced Predictive Modelling in R course will cover different concepts of predictive modelling and almost all the forms of regression analysis with application. You will learn regression in detail including detailed understanding of various regression techniques using case studies. Also, you will be introduced to various other regression techniques which are used in industry. Overall, you will gain expertise in predictive modelling and forecasting. You will also get to implement one project towards the end of the course.
The Topics covered in the webinar are:
1.Advanced Predictive Modelling in R
2. Introduction to Predictive Modelling
3.Beyond OLS: How real life data-set looks like!
4.decoding Forecasting
5.How to Handle real life dataset: Two examples
6.How to Build Models in R: Example
7.Forecasting techniques and Plots
Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would
prefer a hassle free and self paced learning environment, accessible from any part of the world.
The topics related to ' The Whys and Hows of Predictive Modelling ' have extensively been covered in our course ' Advanced Predictive Modelling in R ’.
For more information, please write back to us at [email protected]
Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
#edureka r #R Certification #predictive modelling #R Programming #ARIMA #forecasting techniques #edureka #Data Science Tutorial #R Tutorial #Advanced analytics #Data analytics #Predictive analytics

2015年03月20日

00:00:00 - 00:30:51