time forecasting

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Time Series Forecasting
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Time Series Forecasting is an important area
of Machine Learning that is often Neglected. Time
Series Forecasting uses different Technologies
like Machine learning, Artificial neural
networks, Support vector machines, Fuzzy logic,
Gaussian processes, Hidden Markov models
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What is Time Series? A time series is a sequence
of measurements done over time, usually obtained
at equally spaced intervals, be it daily,
monthly, quarterly or yearly. 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. In other words, a time series is
a sequence of data points being recorded at
specific times.
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• Some of the examples of time series may be
• Daily air temperature or monthly precipitation in
  Bangalore, India
• Annual flow volume of the River Ganga at Patna
• Annual Indian population data
• Daily closing stock prices
• Weekly interest rates

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What is Forecasting?
Forecasting is the process of making predictions
of the future based on past and present data
along with analyzing the trends. Forecasting
involves taking models that fit on historical
data and using them to predict future
observations.
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Time Series Analysis Techniques

• Time Series can be defined as an ordered sequence
  of values of a variable at equally spaced time
  intervals. The motivation to study 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 forecasting,
  monitoring or even feedback and feedforward
  control

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• Kinetic Model The data here is fitted as xt
  f(t). The measurements or observations are seen
  as a function of time.
• Dynamic Model The data here is fitted as xt
  f(xt-1 , xt-2 , xt-3 ).
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Time Series Forecasting Methods Time series
forecasting methods produce forecasts based
solely on historical values and they are widely
used in business situations where forecasts of a
year or less are required. These methods used are
particularly suited to Sales, Marketing, Finance,
Production planning etc. and they have the
advantage of relative simplicity. Time series
forecasting is a technique for the prediction of
events through a sequence of time.
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• The technique is used across many fields of
  study, from geology to economics. The techniques
  predict future events by analyzing the trends of
  the past, on the assumption that the future
  trends will hold similar to historical trends.
  Data is organized around relatively deterministic
  timestamps, and therefore, compared to random
  samples, may contain additional information that
  is tried to extract.
• Time series methods are better suited for
  short-term forecasts (i.e., less than a year).

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• Time series forecasting relies on sufficient past
  data being available and that the data is of a
  high quality and truly representative.
• Time series methods are best suited to relatively
  stable situations. Where substantial fluctuations
  are common and underlying conditions are subject
  to extreme change, then time series methods may
  give relatively poor results.
• Examples may include
• Forecasting the potato yield in tons by state
  each year
• Forecasting unemployment for a state each quarter
• Forecasting the birth rate at all hospitals in a
  city each year

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The plot below depicts the food, beer and wine
sales in the U.S. for the year 2016 till 2020.
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Basic Steps of Time Series Forecasting A  Time
Series Forecasting task usually involves five
basic steps. Step 1 Problem definition. Step 2
Gathering information. Step 3 Preliminary
(exploratory) analysis. Step 4 Choosing and
fitting models. Step 5 Using and evaluating a
forecasting model.
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• There are many statistical techniques available
  for time series forecast however we have found
  few effectives ones which are listed below
• Simple Moving Average (SMA)
• Exponential Smoothing (SES)
• Autoregressive Integration Moving Average (ARIMA)
• Neural Network (NN)
• Croston

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Some of the other Time-series forecasting methods
are Trend Projection This method used the
underlying long-term trend of time series of data
to forecast its future values. Trend and Seasonal
Components Method This method uses seasonal
component of a time series in addition to the
trend component.
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Causal Method This method uses the
cause-and-effect relationship between the
variable whose future values are being forecasted
and other related variables or factors. The
widely known causal method is called regression
analysis, a statistical technique used to develop
a mathematical model showing how a set of
variables is related. This mathematical
relationship can be used to generate forecasts.
There are more complex time-series techniques as
well, such as ARIMA and Box-Jenkins models. In
many modern applications, time series forecasting
uses computer technologies, including
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• Machine learning
• Artificial neural networks
• Support vector machines
• Fuzzy logic
• Gaussian processes
• Hidden Markov models
• Concerns of Time Series Forecasting
• How much data is available and how much data you
  are able to gather? More data is often more
  helpful, offering greater opportunity for
  exploratory data analysis.

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What is the time horizon of predictions that is
required? Shorter time horizons are often easier
to predict with higher confidence. Can forecasts
be updated frequently over time? Updating
forecasts results in more accurate
predictions. Time series data often requires pre
and post processing including cleaning, scaling,
and even transformation.
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Models used for Time series Forecasting
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• Applications of Time Series Forecasting
• Time series Forecasting models are used in
• Finance to forecast stocks performance
• Finance to forecast interest rate
• It is used in forecasting weather
• It is used in Budget Analysis
• It is used in Military planning
• It is used in Workload projections

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