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Search results “Trend analysis and modelling”

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1. Basic Multiplicative Model (TCSI) 2. What are different components like Trend component, cyclic component, seasonal component etc? 3. How to calculate different component in a given series using excel 4. How to forecast using TCSI model (step by step in excel) VSP Group, my partner program. Get connected! https://youpartnerwsp.com/en/join?62916
Views: 11373 Gopal Malakar

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Using dummy variables and multiple linear regression to forecast trend and seasonality
Views: 104072 profMattDean

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This video explains the difference between stochastic and deterministic trends. A simulation is provided at the end of the video, demonstrating the graphical difference between these two types of stochastic process. I also provide code below in Matlab/Octave for anyone wanting to run these simulations. clear; close all; clc; n=10000; % Number of time periods b=1; rho=1; %The parameter on the lagged value of x alpha=0.05; % The slope parameter % Initialise x and y x=zeros(n,1); x(1)=0; y=zeros(n,1); y(1)=0; % Generate the x and y series for i = 2:n x(i)=alpha+rho*x(i-1)+b*randn(); y(i)=alpha*i+b*randn(); end % Plot the x and y series zoom=1.0; FigHandle = figure('Position', [750, 300, 1049*zoom, 895*zoom]); plot(x, 'LineWidth', 1.4) hold on plot(y, 'm','LineWidth', 1.4) Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Views: 68835 Ben Lambert

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( Data Science Training - https://www.edureka.co/data-science ) In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future! Below are the topics we will cover in this live session: 1. Why Time Series Analysis? 2. What is Time Series Analysis? 3. When Not to use Time Series Analysis? 4. Components of Time Series Algorithm 5. Demo on Time Series For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 79500 edureka!

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Time Series Analysis: Introduction to the model; Seasonal Adjustment Method Part 1 of 4
Views: 185522 Simcha Pollack

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QUANTITATIVE METHODS TIME SERIES ANALYSIS

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This is an overview of some basic forecasting methods. These basic forecasting methods are broken into two categories of approaches: quantitative and Qualitative. Quantitative forecasting approaches use historical data and correlative association to make forecasts. Qualitative forecasting approaches look at the opinions of experts, consumers, decision makers and other stakeholders. This video is about basic forecasting methods and covers 9 of the most common approaches. Avercast forecasting software makes good use of these approaches, and is powered by over 200 algorithms. Visit http://www.avercast.com/ for more information on our leading forecasting software.
Views: 107656 Avercast, LLC

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The fourth in a five-part series on time series data. In this video, I explain how to use an additive decomposition model to: - use regression methods to estimate trend - use dummy variables to estimate seasonal influences - forecast with and without seasonal influences
Views: 13762 Jason Delaney

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Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.

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In this video we run a linear regression on a time series dataset with time trend and seasonality dummies. Then, we perform and evaluate the accuracy of an in-sample forecast, as well as perform an out-of-sample (i.e., into the future) forecast. TABLE OF CONTENTS: 00:00 Introduction 00:12 What we will do in this Video 00:40 Data 01:14 Glimpse Data in Excel 01:46 Load Data in Gretl 03:20 Plot Time Series 03:54 Create Additional Variables 04:38 Run Model with All Data 05:34 In-Sample Forecast 06:40 Evaluating Quality of In-Sample Forecast 10:37 Out-of-Sample Forecast
Views: 44492 dataminingincae

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In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 380623 Analytics University

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In this video, you will learn how to find the demand forecast using linear regression.
Views: 70550 maxus knowledge

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This lesson introduces time series data. We then cover several quantitative time series forecasting methods presenting moving average (MA), weighted moving average (WMA) and exponential models. As we present each type of model we show how to develop the model in Excel (Google Forms). https://ericjjesse.wordpress.com/course-introduction/forecasting-and-regression/
Views: 27071 Eric Jesse

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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 176618 MIT OpenCourseWare

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Argonne National Laboratory Future States Lecture, John Krummel

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Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn: 1) (00:11) Forecasting using Regression when we see a trend and belief the trend will extend into the future. Will will predict outside the Experimental Region with the Assumption is that trend continues into future. 2) (00:53) Forecast a Trend using Simple Liner Regression. We use the Data Analysis Regression Feature. 3) (03:22) Learn how to use FORECAST function. 4) (08:57) Forecast a Seasonal Pattern using Multiple Regression and three Categorical Variables for quarter using Multiple Linear Regression. We use the Data Analysis Regression Feature. 5) (12:12) VLOOKUP & MATCH functions with Mixed Cell References to populate new categorical variable columns with the Boolean ones and zeroes. 6) (19:53) Forecast a Trend with a Seasonal Pattern using Multiple Regression and three Categorical Variables for quarter and one quantitative variable using Multiple Linear Regression. We use the Data Analysis Regression Feature. 7) Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 66335 ExcelIsFun

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Be sure to visit my website at: https://sites.google.com/view/statistics-for-the-real-world/home This video is the first of several on ARIMA modeling using IBM SPSS. Specifically, it focuses on how to identify AR and MA processes. It also covers the topic of stationarity and identification of trending. (Be sure to check out the next video in the series on estimating ARIMA model parameters using SPSS syntax. Example syntax can be accessed through links in the video description) A copy of the original dataset can be downloaded here: https://drive.google.com/open?id=1gT2FbgUeZHIAG5vKctUrJWM--pbkXWRk The demonstrations provided in this video come from Chapter 18 of Tabachnick & Fidell's text, Using Multivariate Statistics (6th edition; https://www.pearson.com/us/higher-education/program/Tabachnick-Using-Multivariate-Statistics-6th-Edition/PGM332849.html) The chapter is downloadable from the textbook website at: http://media.pearsoncmg.com/ab/ab_tabachnick_multistats_6/datafiles/M18_TABA9574_06_SE_C18.pdf For more details of the computations involved, you can go here: https://youtu.be/WlSz0Ji19PM
Views: 14840 Mike Crowson

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Views: 28559 Paula Guilfoyle

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This is the part of Lecture series from SabberFoundation. Lectured by Md. Sabber Ahamed, Jahangirnagar University, Bangladesh. In Facebook : http://www.facebook.com/groups/learnGISforUS/
Views: 117949 SabberFoundation

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What is the difference between Autoregressive (AR) and Moving Average (MA) models? Explanation Video: https://www.youtube.com/watch?v=2kmBRH0caBA
Views: 19789 The Data Science Show

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This video is on Panel Data Analysis. Panel data has features of both Time series data and Cross section data. You can use panel data regression to analyse such data, We will use Fixed Effect Panel data regression and Random Effect panel data regression to analyse panel data. We will also compare with Pooled OLS , Between effect & first difference estimation For Analytics study packs visit : https://analyticuniversity.com Time Series Video : https://www.youtube.com/watch?v=Aw77aMLj9uM&t=2386s Logistic Regression using SAS: https://www.youtube.com/watch?v=vkzXa0betZg&t=7s Logistic Regression using R : https://www.youtube.com/watch?v=nubin7hq4-s&t=36s Support us on Patreon : https://www.patreon.com/user?u=2969403
Views: 72847 Analytics University

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Time Series in R, Session 1, part 1 (Ryan Womack, Rutgers University) http://libguides.rutgers.edu/data twitter: @ryandata Fixed the script and provided new locations for downloads at https://ryanwomack.com/TimeSeries.R https://ryanwomack.com/data/UNRATE.csv https://ryanwomack.com/data/CPIAUCSL.csv
Views: 112303 librarianwomack

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Views: 107413 StataCorp LLC

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I demonstrate how to perform a linear regression analysis in SPSS. The data consist of two variables: (1) independent variable (years of education), and (2) dependent variable (weekly earnings). It was hypothesized that years of education would be positively associated with weekly earnings. Additionally, the slope (unstandardized beta weight) and intercept (value of Y when X is 0) were identified and interpreted.
Views: 510659 how2stats

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In this video, I have briefly shown how to prepare a dataset for trend analysis using non-parametric approach. https://drive.google.com/file/d/1HVP81cxEpkPwfJ9qksCiV7WrM9p9bwXJ/view?usp=drive_web #SharingisCaring #PleaseSubscribe Check this link to watch similar videos: https://www.youtube.com/user/fitsalem/videos?view_as=subscriber
Views: 13459 Tech-tutor with fitsum

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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 51491 MATLAB

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Simple Linear Regression using Microsoft Excel

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Watch this brief (10 minutes or so!!) video tutorial on how to do all the calculations required for a Time Series analysis of data on Microsoft Excel. Try and do your best to put up with the pommie accent. The data for this video can be accessed at https://sites.google.com/a/obhs.school.nz/level-3-statistics-and-modelling/time-series
Views: 108328 mrmathshoops

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In this video, I show how to use R to fit a linear regression model using the lm() command. I also introduce how to plot the regression line and the overall arithmetic mean of the response variable, and I briefly explain the use of diagnostic plots to inspect the residuals. Basic features of the R interface (script window, console window) are introduced. The R code used in this video is: data(airquality) names(airquality) #[1] "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day" plot(Ozone~Solar.R,data=airquality) #calculate mean ozone concentration (na´s removed) mean.Ozone=mean(airquality\$Ozone,na.rm=T) abline(h=mean.Ozone) #use lm to fit a regression line through these data: model1=lm(Ozone~Solar.R,data=airquality) model1 abline(model1,col="red") plot(model1) termplot(model1) summary(model1)
Views: 333419 Christoph Scherber

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R, detrend, seasonality, ARIMA model, stl, tsdisplay, findfrequency,lm. In this video you can learn how to split data into trend and seasonal component, how to remove linear trend and seasonal effect from data, so that you can fit an appropriate model.
Views: 5468 DATA MODELLING

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Views: 25846 Sarveshwar Inani

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Discover the basics of using the -xtmixed- command to model multilevel/hierarchical data using Stata. If you'd like to see more, please visit the Stata Blog: http://blog.stata.com/2013/02/04/multilevel-linear-models-in-stata-part-1-components-of-variance Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 96181 StataCorp LLC

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ARMA/ARIMA is a method among several used in forecasting variables. Uses the information obtained from the variables itself to forecast its trend. The variable is regressed on its own past values. Based on univariate analysis. Knowing and analysing the probabilistic, or stochastic, properties of variables. Designed to forecast future movements. Uses the philosophy “let the variable speak for itself”. This concept is very relevant because it helps investors, government regulators, policy makers and relevant stakeholders take informed decision. In essence, information relating to the series are obtained from the series itself. The Box-Jenkins type time series models allow Yt to be explained by past, or lagged, values of Y itself and stochastic error terms (innovations or shocks). For this reason, ARMA models are sometimes called atheoretic models because they are not derived from any economic theory. The series is simply explaining itself using its historical data. ARMA is composed of two distinct models which explains the behaviour of a series from two different perspectives: the autoregressive (AR) models and the moving average (MA) models. We will also show that these models move in opposite directions of one another. Distinction between ARMA and ARIMA is the integration component which brings us back to the subject of stationarity. In reality, most economic variables are non-stationary hence they have to go through a transformation process called differencing before they become stationary. The transforming process is also called integration. So ARIMA informs the researcher or reader that the series in question has gone through an integration process before being used for any analysis. Hence, the moment a nonstationary variable is differenced before becoming stationary, such is known as an integrated variable. Since the essence of engaging an ARIMA model is to forecast a series, the B-J methodology uses four steps: identification, estimation, diagnostics and forecasting. Follow up with soft-notes and updates from CrunchEconometrix: Website: http://cruncheconometrix.com.ng Blog: https://cruncheconometrix.blogspot.com.ng/ Forum: http://cruncheconometrix.com.ng/blog/forum/ Facebook: https://www.facebook.com/CrunchEconometrix YouTube Custom URL: https://www.youtube.com/c/CrunchEconometrix Stata Videos Playlist: https://www.youtube.com/watch?v=sTpeY31zcZs&list=PL92YnqQQ1gbjyoGWR2VUemNPU93yivXZx EViews Videos Playlist: https://www.youtube.com/watch?v=znObTs4aJA0&list=PL92YnqQQ1gbghRSJURtz08AZdImbge4h-
Views: 13451 CrunchEconometrix

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Views: 38071 Hashtag 4You

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This video demonstrates how to make a graph in Excel with multiple series of data on the same set of axes.
Views: 10925 JesseOswald

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Views: 8264 Sayed Hossain

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In this clip I demonstrate how to use EVIEWS for Forecasting
Views: 110289 Ralf Becker

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Using SPSS to generate prediction equations using linear regression
Views: 66731 ProfessorAmiGates

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Hi Friends, Thanks for watching my video. For Instructor lead live Tableau online training, feel free to touch base with me on Cell# +919583261771 or mail me on: [email protected] Cheers, Amit Kumar Prince Tech Solutions
Views: 14014 Prince Tech Solutions

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