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Time Series Analysis, Components and Application in Forecasting
Time series analysis involves analyzing data collected over time. A time series is a set of observations made at regular intervals. There are four main components of a time series: secular trend, seasonal variation, cyclical variation, and irregular variation. Time series analysis has applications in forecasting, such as for economic forecasting, sales forecasting, and stock market analysis. Techniques for time series analysis include Box-Jenkins ARIMA models, Box-Jenkins multivariate models, and Holt-Winters exponential smoothing.
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Time Series Analysis, Components and Application in Forecasting
- 2. Contents Introduction Definition Components of time series analysis Applications and techniques Applications in forcastic
- 3. TIME SERIES ANALYSIS >Future is a very necessary for the every business firm,every Govt,institute,every individual and for every country. >As a like every business is doing planning for possibilities of its financial resources & Sale and for maximization its profit.
- 4. DEFINITION “ A timeseries is a set of observation taken at specified times, usually at equal intervals ‘’
- 5. Components of timeseries >The change which are being in time series, they are affected by economic, social,natural industrial & political reasons,these reasons are called components of time series * Secular trend * Seasonal variation * Cyclical variation * Irregular Variation
- 6. SECULAR TREND • Theincrease of decrease in the movement of a time series is called secular trend • Increase in population • Change in technical progress • Large scare shifts in consumers demands
- 8. SEASONAL VARIATION • Seasonalvariation are short –term fluctuation in a time series which occur periodically in a year • More woolen clothes are sold in winter than in the season of summer • Each year more ice creams are sold in summer and very little in winter season
- 10. CYCLICAL VARIATION • Cyclicalvariation are recurrent upword or downward movement in a time series but the Period of cycle is greater than a year,Also these variation are not regular as seasonal variation • Business cycle
- 12. IRREGULAR VARIATION • Irregularvariation are fluctuation in time series that are short in duration,erratic nature and follow no regularity in the occurrence pattern • Flood’s • Earthquakes • Wars
- 14. Applications: the usageof time series models is twofold Obtain an understanding of the underlying forces and structure that produced the observed data. Fit a model and proceed to forcasting , monitoring or even feedback and feedforward control.
- 15. Techniques of timeseries analysis The fitting of time series models can be an ambitious undertaking . There are many methods of modal fitting including the following: box- jenkins ARIMA models Box- jenkins multivariate models Holt-winters exponential smoothing_(single,double,triple).
- 16. APPLICATION OF TIMESERIES • Economic forecasting • Sale forecasting • Budgetory analysis • Stock market analysis • Yield projection • Process and quality control • Inventory studies • Workload projection • Utility studies • Senses analysis
- 17. CONCLUSION > Time seriesanalysis is a most for every company to understand seasonality,cyclicality,trend and randomness in the sales and attributes.in the coming blogs we will learn more on how to perform time series analysis with R,python and Hadoop.
- 18. REFERENCE “ Time seriesanalysis : The components That Define It/ by OBSC open data science https://medium.com/@ODSc/time series Analysis -the components-that-define-it- 2234b76ce544
- 19.