A Study on Performance Analysis of Different Prediction Techniques in Predict...IJRES Journal
Time series data is a series of statistical data that is related to a specific instant or a specific time period. Here, the measurements are recorded on a regular basis such as monthly, quarterly and yearly. Most of the researchers have used one of the prediction techniques in prediction of time series data. But, they have not tested all prediction techniques on same data set. They have not even compared the performance of different prediction techniques on the same data set. In this research work, some well known prediction techniques have been applied in the same time series data set. The average error and residual analysis have been done for each and every applied technique. One technique has been selected based on the minimum average error and residual analysis among the all applied techniques. The residual analysis comprises of absolute residual, maximum residual, median of absolute residual, mean of absolute residual and standard deviation. To finalize the algorithm, same procedure has been applied on different time series data sets. Finally, one technique has been selected which has been given minimum error and minimum value of residual analysis in most cases.
A Study on Performance Analysis of Different Prediction Techniques in Predict...IJRES Journal
Time series data is a series of statistical data that is related to a specific instant or a specific time period. Here, the measurements are recorded on a regular basis such as monthly, quarterly and yearly. Most of the researchers have used one of the prediction techniques in prediction of time series data. But, they have not tested all prediction techniques on same data set. They have not even compared the performance of different prediction techniques on the same data set. In this research work, some well known prediction techniques have been applied in the same time series data set. The average error and residual analysis have been done for each and every applied technique. One technique has been selected based on the minimum average error and residual analysis among the all applied techniques. The residual analysis comprises of absolute residual, maximum residual, median of absolute residual, mean of absolute residual and standard deviation. To finalize the algorithm, same procedure has been applied on different time series data sets. Finally, one technique has been selected which has been given minimum error and minimum value of residual analysis in most cases.
Holt-Winters forecasting allows users to smooth a time series and use data to forecast selected areas. Exponential smoothing assigns decreasing weights and values against historical data to decrease the value of the weight for the older data, so more recent historical data is assigned more weight in forecasting than older results. The right augmented analytics provides user-friendly application of this method and allow business users to leverage this powerful tool.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
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Holt-Winters forecasting allows users to smooth a time series and use data to forecast selected areas. Exponential smoothing assigns decreasing weights and values against historical data to decrease the value of the weight for the older data, so more recent historical data is assigned more weight in forecasting than older results. The right augmented analytics provides user-friendly application of this method and allow business users to leverage this powerful tool.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
2. Introduction to Time Series Analysis
• A time-series is a set of observations on a quantitative
variable collected over time.
• Examples
– Dow Jones Industrial Averages
– Historical data on sales, inventory, customer counts,
interest rates, costs, etc
• Businesses are often very interested in forecasting time
series variables.
• Often, independent variables are not available to build a
regression model of a time series variable.
• In time series analysis, we analyze the past behavior of
a variable in order to predict its future behavior.
3. Methods used in Forecasting
• Regression Analysis
• Time Series Analysis (TSA)
– A statistical technique that uses time-
series data for explaining the past or
forecasting future events.
– The prediction is a function of time
(days, months, years, etc.)
– No causal variable; examine past behavior
of a variable and and attempt to predict
future behavior
4. Components of TSA
• Time Frame (How far can we predict?)
– short-term (1 - 2 periods)
– medium-term (5 - 10 periods)
– long-term (12+ periods)
– No line of demarcation
• Trend
– Gradual, long-term movement (up or down) of
demand.
– Easiest to detect
5. Components of TSA (Cont.)
• Cycle
– An up-and-down repetitive movement in demand.
– repeats itself over a long period of time
• Seasonal Variation
– An up-and-down repetitive movement within a trend
occurring periodically.
– Often weather related but could be daily or weekly
occurrence
• Random Variations
– Erratic movements that are not predictable because
they do not follow a pattern
6. Time Series Plot
Actual Sales
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Time Period
Sales
(in
$1,000s)
7. Components of TSA (Cont.)
• Difficult to forecast demand because...
– There are no causal variables
– The components (trend, seasonality,
cycles, and random variation) cannot
always be easily or accurately
identified
8. Some Time Series Terms
• Stationary Data - a time series variable exhibiting
no significant upward or downward trend over
time.
• Nonstationary Data - a time series variable
exhibiting a significant upward or downward trend
over time.
• Seasonal Data - a time series variable exhibiting
a repeating patterns at regular intervals over
time.
9. Approaching Time Series Analysis
• There are many, many different time series
techniques.
• It is usually impossible to know which technique
will be best for a particular data set.
• It is customary to try out several different
techniques and select the one that seems to
work best.
• To be an effective time series modeler, you need
to keep several time series techniques in your
“tool box.”
10. Measuring Accuracy
• We need a way to compare different time series
techniques for a given data set.
• Four common techniques are the:
– mean absolute deviation,
– mean absolute percent error,
– the mean square error,
– root mean square error.
MAD =
Y Y
i i
i
n
n
1
MSE =
Y Y
i i
i
n
n
2
1
MSE
RMSE
n
i i
i
i
n 1 Y
Ŷ
Y
100
=
MAPE
• We will focus on MSE.
11. Extrapolation Models
• Extrapolation models try to account for the past
behavior of a time series variable in an effort to
predict the future behavior of the variable.
, , ,
Y Y Y Y
t t t t
f
1 1 2
12. Moving Averages
Y
Y Y Y
t t-1 t- +1
t
k
k
1
u No general method exists for determining k.
u We must try out several k values to see what works best.
13. Weighted Moving Average
• The moving average technique assigns equal weight
to all previous observations
Y
1
Y
1
Y
1
Y
t t-1 t- -1
t k
k k k
1
u The weighted moving average technique allows for
different weights to be assigned to previous
observations.
Y Y Y Y
t t-1 t- -1
t k k
w w w
1 1 2
where 0 and
w w
i i
1 1
u We must determine values for k and the wi
14. Exponential Smoothing
( )
Y Y Y Y
t t t t
1
where 0 1
u It can be shown that the above equation is equivalent to:
( ) ( ) ( )
Y Y Y Y Y
t t t t
n
t n
1 1
2
2
1 1 1
15. Seasonality
• Seasonality is a regular, repeating
pattern in time series data.
• May be additive or multiplicative in
nature...
16. M u ltip licative S easo n al E ffects
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5
T im e Pe r io d
Ad d itive S easo n al E ffects
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5
T im e Pe r io d
Stationary Seasonal Effects
17. Trend Models
• Trend is the long-term sweep or general
direction of movement in a time series.
• We’ll now consider some nonstationary time
series techniques that are appropriate for
data exhibiting upward or downward trends.
18. The Linear Trend Model
Y X
t b b t
0 1 1
where X1t
t
For example:
X X X
1 1 1
1 2 3
1 2 3
, , ,
19. The TREND() Function
TREND(Y-range, X-range, X-value for prediction)
where:
Y-range is the spreadsheet range containing the dependent Y
variable,
X-range is the spreadsheet range containing the independent X
variable(s),
X-value for prediction is a cell (or cells) containing the values for
the independent X variable(s) for which we want an estimated value
of Y.
Note: The TREND( ) function is dynamically updated whenever any inputs to
the function change. However, it does not provide the statistical information
provided by the regression tool. It is best two use these two different
approaches to doing regression in conjunction with one another.
20. The Quadratic Trend Model
Y X X
t b b b
t t
0 1 1 2 2
where X and X
1 2
2
t t
t t
21. Combining Forecasts
• It is also possible to combine forecasts to create a
composite forecast.
• Suppose we used three different forecasting methods
on a given data set.
u Denote the predicted value of time period t using
each method as follows:
F F F
t t t
1 2 3
, , and
u We could create a composite forecast as follows:
Y F F F
t b b b b
t t t
0 1 1 2 2 3 3