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vikas_ohri2
Explorer

Forecasting is a vital cog in effective business planning be it forecasts of stock requirements or call volumes of a call center.


Obtaining the seasonal , trend and cyclic components or decomposing the time series data can provide insights into the business analysis for enterprise functions varying from sales forecasting, demand planning or customer satisfaction. Sharing a data product http://ohrivikas.weebly.com/forecasting.html which provides these insights.


Learning from the past data patterns to extrapolate the future predictions  and at the same time ignoring random data or data that is unlikely to repeat is the key for useful forecasting.


Time series data like sales data of quantities of items units sold can be regarded as comprising of a seasonal component, a trend-cycle (contains both trend & cycle) component and anything else regarded as remainder component. These components can be model as additive or multiplicative  i.e. they add up or multiply to constitute the time series data.


Time series  data  for example Product Units Sold per month can exhibit a huge variety of trends, seasonality and cyclic patterns  in the data. Business analysts are continuously trying to identify factors causing Change  ( either increase or decrease) in direction of trend.It is useful to extract those patterns to understand the business and improve forecasts. Since Forecast is a prediction of values in future , it is useful to attribute a confidence interval around these values . Shared below is link to application i created for quick visual analysis of time series data . Forecasting of your enterprise data with Exponential smoothing and Seasonal and Trend decomposition using Loess  can be done as well Time series decomposition by this tool.

http://ohrivikas.weebly.com/forecasting.html

Past 25 years of monthly utility average price of California have been already included to study the data, from  data source U.S. Energy Information Administration (EIA) .  Monthly trends plots show power prices have increased in summer months by providing the increase or decrease of average monthly values compared to data average. Currently it can also accept, spreadsheet .XLSX format with ,monthly periodic data of uni variate time series for data analysis purposes, as shown in the table picture below.

ProductYearJanFebMarAprMayJunJulAugSepOctNovDec
A20141008987931011151169085787560
A2011899087911021031259680897167
A201210279839510592785993718072
A20139910378998478869073897964
C20141008980779070746372788598
C20139599757680656870617488100
C20119599757680656870617488100
D20141008987931011151169085787560


Using Native ECC & Netweaver tools creating time series data like quantities of product sold by time period (say monthly),  as shown in above table  is fairly easy and commonly available in out of the box reports.


Few terms that may help the reader & user are explained below.

A trend exists when there is a long-term increase or decrease in the data. It does not need to be linear. A seasonal pattern exists when a series is influenced by seasonal factors (e.g., the quarter of the year, the month, or day of the week). Seasonality is always of a fixed and known period.A cyclic pattern exists when data exhibit rises and falls that are not of fixed period over a fairly long time periods usually in years. Average length of cycles is longer than the length of a seasonal pattern . Seasonally adjusted data is if the seasonal component is removed from the original data.

Importantly note, although in the data product  http://ohrivikas.weebly.com/forecasting.html , exponential forecasting option automatically chooses best fitting model, there are several  models or methods to do forecasting , which one to use is essentially determined by the data characteristics and out of sample accuracy testing .