Basic Concepts, Components of time series. The trend, Fitting of trend by least square method and moving average method, uses of time series in business.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Basic Concepts, Components of time series. The trend, Fitting of trend by least square method and moving average method, uses of time series in business.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
2. Introduction
• A time series consists of a set of
observations which are measured at
specified (usually equal) time intervals.
• Time series analysis attempts to identify
those factors that exert an influence on the
values in the series. Once these factors
are identified, the time series may be used
for both short-term and long-term
forecasting.
3. A several of time series
year
GDP
(100 million
yuan)
Total
population
(year-end)
(10000
persons)
Natural Growth
Rate of
Population
(‰)
Household
consumption
expenditures
(yuan)
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
18547.9
21617.8
26638.1
34634.4
46759.4
58478.1
67884.6
74772.4
79552.8
80471.6
114333
115823
117171
118517
119850
121121
122389
123626
124810
125924
14.39
12.98
11.60
11.45
11.21
10.55
10.42
10.06
9.53
9.48
803
896
1070
1331
1781
2311
2726
2944
3094
3130
4. Time series components
The four components usually identified are:
• Secular trend ----the underlying
movement of the series
• Seasonal variation
• Cyclical variation
• Irregular variation
While it is possible to break down a time
series into these four components, the task
is not always simple.
6. Secular trend
• The secular trend is the long-term growth or
decline of a series. It is decided by the property of
the variable itself.
• In typical economic contexts, ‘long-term’ may mean
10 years or more. Essentially, the period should be
long enough for any persistent pattern to emerge.
• Secular trends allow us to look at past patterns or
trends and use these to make some prediction
about the future.
• In some situations it is possible to isolate the effect
of secular trends from the time series and hence
make studies of the other components easier.
8. Seasonal variation
• The seasonal variation of a time series is a pattern
of change that recurs regularly over time.
Seasonal patterns typically are one year long; that
is, the pattern starts repeating itself at a fixed time
each year.
• While variations may recur every year, the
concept of seasonal variation also extends to
those patterns that occur monthly, weekly, daily or
even hourly.
• Time series graphs may be seasonally adjusted or
deseasonalized by “seasonal index” when the
seasonal variation of it is very strong. Such
graphs give us a true picture of genuine
movements in the time series after the seasonal
effects have been removed.
9. Examples of seasonal variation
• Air conditioner sales are greater in the
summer months.
• Heater sales are greater in the winter months
• The total number of people seeking work is
large at the end of each year when students
leave school
• Motels, hotels and camping grounds have a
greater volume of customers in holiday
seasons
• Train ticket sales increase dramatically
during festive seasons
10. • Medical practitioners report a substantial
increase in the number of flu cases each
winter
• Liquor outlets undergo increased sales
during festive seasons
• Airline ticket sales (and price!) increase
during school holidays
• The amount of electricity and water used
varies within each 24-hour period
• The volume of work for tax agents increases
dramatically around the time when income
tax forms have to be filed.
11. Cyclical variation
• In a similar manner to seasonal variations,
cyclical variations have recurring patterns,
but have a longer and move erratic time
scale.
• Unlike seasonal variation, there is no
guarantee that there will be any regularly
recurring pattern of cyclical variation. It is
usually impossible to predict just how long
these periods of expansion and contraction
will be.
12. Examples of causes
of cyclical variation
• Floods
• Earthquakes / hurricanes
• Droughts
• Wars
• Changes in interest rates
• Major increases or decreases in the
population
13. • The opening of a new shopping
complex
• The building of a new airport
• Economics depressions or recessions
• Major sporting events, such as the
Olympic Games
• Changes in consumer spending (i.e.
lack of confidence)
• Changes in government monetary
policy
14. Irregular variation
• Irregular variation in the time series occurs
varying (usually short) periods. It follows no
regular pattern and is by nature
unpredictable. It usually occurs randomly
and may be linked to events that also occur
randomly.
• It cannot be explained mathematically. In
general, if the variation in a time series
cannot be accounted for by secular trend, or
by seasonal or cyclical variation, then it is
usually attributed to irregular variation.
15. Examples of events that
might cause irregular variation
• The assassination (or disappearance) of a
country’s leader
• Short-term variation in the weather, such as
unseasonably warm winters (they may affect
sales of certain products)
• Sudden changes in interest rates
• The collapse of large (or even small)
companies
16. • Strikes (e.g. a strike by airline pilots
affects many people working in the travel
industry)
• A government calling an unexpected
election
• Sudden shifts in government policy
• Natural disasters
• Dramatic changes to the stock market
• The effect of war in the Middle East on
petrol prices around the world
17. Measurement of secular trend
• Measurement of secular trend can be
somewhat subjective, depending on the
technique used to measure it.
• The methods used to measure it.
1. semi-averages
2. least-squares linear regression
3. moving averages
4. exponential smoothing
5. growth model
20. Least-squares linear regression
• A more sophisticated way of fitting a
straight line to a time series is to use
the method of least-squares linear
regression
• In this case, the observations are the
(dependent) y-variables and time is the
(independent) x-variable
• Since in this case the x-variable is time
units, the calculations may be
simplified as follows
22.
x
46
.
503
36
.
7437
ŷ
36
.
7437
11
81811
y
x
b
y
a
74
.
506
110
55381
x
xy
x
x
y
y
x
x
b
bx
a
ŷ
2
2
Excel
23. year x
Number of house
y
x2 xy
1995 -7 49 49 -343
1996 -5 133 25 -665
1997 -3 69 6 -207
1998 -1 170 1 -170
1999 1 133 1 133
2000 3 175 9 525
2001 5 152 25 760
2002 7 185 49 1295
total 0 1066 168 1328
n=偶数
25. Moving averages
• The method of moving averages is based
on the premise that, if the values in a time
series are averaged over a sufficient
period, the effect of short-term variations
will be reduced. That is, short-term cyclical,
seasonal and irregular variations will be
smoothed out, leaving an apparently
smooth graph to show the overall trend.
26. Calculation of the 3-year moving averages for data
year Number of sales
3-year moving
total
3-year moving
average
1994 1011 ---- ----
1995 1031 3018 1006
1996 976 3027 1009
1997 1020 3191 1064
1998 1195 3389 1130
1999 1174 3630 1210
2000 1261 3765 1255
2001 1330 3975 1325
2002 1384 ---- ----
27. Calculation of the 4-year moving averages for data
year y
4-year
total
4-year
average
4-year
total
4-year
average
Moving
average
1992 47.6 ---- ---- ---- ---- ----
1993 48.9 ---- ---- 203.3 50.8 ----
1994 51.5 203.3 50.8 213.6 53.4 52.1
1995 55.3 213.6 53.4 226.4 56.6 55.0
1996 57.9 226.4 56.6 240.2 60.0 58.3
1997 61.7 240.2 60.0 255.1 63.8 61.9
1998 65.3 255.1 63.8 273.3 68.3 66.0
1999 70.2 273.3 68.3 296.3 74.1 71.2
2000 76.1 296.3 74.1 324.2 81.0 77.6
2001 84.7 324.2 81.0 ---- ---- ----
2002 93.2 ---- ---- ---- ---- ----
28. Exponential smoothing
• Exponential smoothing is a method for
continually revising an estimate in the light of
more recent trends. It is based on averaging (or
smoothing) the past values in a series in an
exponential manner.
• Recurrence relation: Sx=αyx+(1-α)Sx-1
where: Sx= the smoothed value for observation x
yx= the actual value of observation x
Sx-1= the smoothed value previously
calculated for observation (x-1)
α= the smoothing constant , (1-α) is
referred to as resistant coefficient where 0≤α≤1
• Generally, we choose: S1=y1 , so S2=αy2+ (1-α) S1
30. Actual data
Exponential smoothing
trend curve (α=0.40)
Excel
The exponential model uses
the current smoothed
estimate as a forecast for
future years. In this case, we
would therefore forecast
average daily sales of milk
to be 82.47L in 2003
31. The smoothing constant ----α
• The selection of the most suitable value of α is not easy. The
greater α is the more important recent trends are. Generally
the value of α is chosen rather subjectively and However,
the following criteria are useful:
1. suppose that the time series has strong irregular
variation, or a seasonal variation causing wide swings,
which it is desired to suppress. Then we might want to take
more account of past trends of the series than recent trends.
In this case, the value of α could be set small (say, =0.1) so
that the history dominates the value of the smoothed
observation.
2. suppose that the time series has little variation. Then we
might want to take more account of recent observations
than those in the past. In this case, the value of α could be
set large (say, =0.9). Recent observations will dominate the
value of the smoothed observation, with previous values
providing merely a kind of background stability.
Sx=αyx+ (1-α) Sx-1
32. Growth model
• Suppose that we note from a graph of the data that
the trend appears to be exponential. In this case, a
growth model may be appropriate. A growth model is
one that takes account of this exponential trend.
• Suppose that we have a time series in which time is
represented by the variable x and the corresponding
observations are represented by the variable y.
Further, suppose that we feel that the values of y are
rising exponentially in relation to x. Then we may fit
the model:
y
of
value
predicted
the
is
y
and
constants
are
b
and
a
:
where ˆ
ae
ŷ
so
e
y bx
error
x
35. year 1997 1998 1999 2000 2001 2002
Sales (y) 127 130 148 160 185 220
x 1 2 3 4 5 6
Z=lny 4.844 4.868 4.997 5.075 5.220 5.394
The least-squares regression line of z on x
z=4.678+0.111x
c1=4.678 c2=0.111
a=e4.678 =107.55 b=0.111
)
(
111
.
0
111
.
0
55
.
107
ˆ
55
.
107
ˆ
year
x
e
y
or
e
y
Homework:S368
11.3, 11.6, 11.8, 11.19, 11.21
Excel
36. Class work
Output of automobile made in China from
1991 to 2008
year
Output
(10 thousands)
year
Output
(10 thousands)
1991
1992
1993
1994
1995
1996
1997
1998
1999
17.56
19.63
23.98
31.64
43.72
36.98
47.18
64.47
58.35
2000
2001
2002
2003
2004
2005
2006
2007
2008
51.40
71.42
106.67
129.85
136.69
145.27
147.52
158.25
163.00
1.Find the 3-year
moving average for
output of auto in the
table
2.Find the least-
squares regression
line of output of auto
in the table
3.Use the exponential
smoothing model in
the table to forecast
the average output of
auto in 2009 (α=0.4)
37. The average retail price of one dozen eggs
in Hobart at 30 June is shown below at
each of the 5-year intervals between 1971
and 1996. Use the growth model (use the
last two digits of the year, i.e. 71, 76, etc.) to
predict the price of eggs (to the nearest cent)
in Hobart on 30 June 2001
Year 1971 1976 1981 1986 1991 1996
Price($) 0.70 1.08 1.63 2.02 2.39 2.75