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Time Series Chapter In Single Video | CA Foundation Statistics Chapter no 19 | ICAI | Chandan Poddar
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In This Video We Will Discuss the Time Series Chapter all Concepts, Problems and Examples for CA Foundation and CA Foundation Statistics Chapter no 19 Has been discussed by Chandan Poddar Sir For CA Foundation Grooming Education.
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⚫ Index Number Chapter in Single Video:-
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Topic Covered:
Introduction
Graphic method
Methods of semi averages
Moving average method 3 years moving average
Four years moving average
Method of least square
Time Stamp:
0:00 Intro
0:15 Time Series
2:10 Graphic Method
12:42 Moving Average Method
16:40 4 Years Moving Average
a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test relationships between one more different time series, this type of analysis is not usually called "time series analysis," which refers in particular to relationships between different points in time within a single series. Interrupted time series analysis is used to detect changes in the evolution of a time series from before to after some intervention which may affect the underlying variable.
Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility).
Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in the frequency domain.
CA foundation: Grooming education academy is engaged in providing qualitative commerce education to society at concessional prices. We believe in Research, reasoning and result and thus our learners admit our USP of: "Pioneer in developing concepts". We provide the complete tutorials for CA foundation.
To know about the courses and books that best suits your requirement call at our helpline number: 𝟖𝟖𝟖 𝟖𝟖𝟖 𝟎𝟒𝟎𝟐
#CAFoundation #Timeseries #Statistics #ChandanPoddar #ICAI
⚫ CA Foundation 9.0:-
⚫ CA Foundation Individual Courses:-
⚫ Perfect Revision: CA Foundation Revision book:-
⚫ CA Foundation Perfect Practice Books:-
⚫ Free All India Challenge Test Series:-
⚫ Download Our Escholars AI App:-
Watch Next:-
⚫ Index Number Chapter in Single Video:-
⚫ How To Find Values of Log & Antilog Complete Chapter:-
Topic Covered:
Introduction
Graphic method
Methods of semi averages
Moving average method 3 years moving average
Four years moving average
Method of least square
Time Stamp:
0:00 Intro
0:15 Time Series
2:10 Graphic Method
12:42 Moving Average Method
16:40 4 Years Moving Average
a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test relationships between one more different time series, this type of analysis is not usually called "time series analysis," which refers in particular to relationships between different points in time within a single series. Interrupted time series analysis is used to detect changes in the evolution of a time series from before to after some intervention which may affect the underlying variable.
Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility).
Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in the frequency domain.
CA foundation: Grooming education academy is engaged in providing qualitative commerce education to society at concessional prices. We believe in Research, reasoning and result and thus our learners admit our USP of: "Pioneer in developing concepts". We provide the complete tutorials for CA foundation.
To know about the courses and books that best suits your requirement call at our helpline number: 𝟖𝟖𝟖 𝟖𝟖𝟖 𝟎𝟒𝟎𝟐
#CAFoundation #Timeseries #Statistics #ChandanPoddar #ICAI
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